Amazon Web Services SAA-C03 Dumps

This answer is correct because it provides high availability at the node level and at the Region level for the ElastiCache for Redis solution. A Multi-AZ Redis replication group consists of a primary cluster and up to five read replica clusters, each in a different Availability Zone. If the primary cluster fails, one of the read replicas is automatically promoted to be the new primary cluster. A Redis replication group with shards enables partitioning of the data across multiple nodes, which increases the scalability and performance of the solution. Each shard can have one or more replicas to provide redundancy and read scaling.

References:

To ingest, transform, and query real-time streaming data from multiple sources, Amazon Kinesis and Amazon MSK are suitable solutions. Amazon Kinesis Data Streams can stream the data from various sources and integrate with other AWS services. Amazon Kinesis Data Analytics can transform the data using SQL or Apache Flink. Amazon Kinesis Data Firehose can write the data to Amazon S3 or other destinations. Amazon Athena can query the transformed data from Amazon S3 using standard SQL. Amazon MSK can stream the data using Apache Kafka, which is a popular open-source platform for streaming data. AWS Glue can transform the data using Apache Spark or Python scripts and write the data to Amazon S3 or other destinations. Amazon Athena can also query the transformed data from Amazon S3 using standard SQL.

References:

What Is Amazon Kinesis Data Streams?

What Is Amazon Kinesis Data Analytics?

What Is Amazon Kinesis Data Firehose?

What Is Amazon Athena?

What Is Amazon MSK?

What Is AWS Glue?

The Kubernetes Cluster Autoscaler is a component that automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled onto other nodes. It uses Auto Scaling groups to scale up or down the nodes according to the demand and capacity of your cluster1.

By using the Kubernetes Cluster Autoscaler in your Amazon EKS cluster, you can achieve the following benefits:

You can improve the performance and availability of your container applications by ensuring that there are enough nodes to run your pods and that there are no idle nodes wasting resources.

You can reduce the administrative overhead of managing your cluster size manually or using custom scripts. The Cluster Autoscaler handles the scaling decisions and actions for you based on the metrics and events from your cluster.

You can leverage the integration of Amazon EKS and AWS Auto Scaling to optimize the cost and efficiency of your cluster. You can use features such as launch templates, mixed instances policies, and spot instances to customize your node configuration and save up to 90% on compute costs2

s.aws.amazon.com/eks/latest/userguide/horizontal-pod-autoscaler.html

Horizontal pod autoscaling is a feature of Kubernetes that automatically scales the number of pods in a deployment, replication controller, or replica set based on that resource’s CPU utilization. It requires a metrics source such as the Kubernetes Metrics Server to provide CPU usage data1. Cluster autoscaling is a feature of Kubernetes that automatically adjusts the number of nodes in a cluster when pods fail or are rescheduled onto other nodes. It requires an integration with AWS Auto Scaling groups to manage the EC2 instances that join the cluster2. By using both horizontal pod autoscaling and cluster autoscaling, the solution can ensure that Amazon EKS scales in and out according to the workload.

To provide individual and secure URLs for all customers using an API Gateway REST API, you need to do the following steps:

A. Register the required domain in a registrar. Create a wildcard custom domain name in a Route 53 hosted zone and record in the zone that points to the API Gateway endpoint. This step will allow you to use a custom domain name for your API instead of the default one generated by API Gateway. A wildcard custom domain name means that you can use any subdomain under your domain name (such as customer1.example.com or customer2.example.com) to access your API. You need to register your domain name with a registrar (such as Route 53 or a third-party registrar) and create a hosted zone in Route 53 for your domain name. You also need to create a record in the hosted zone that points to the API Gateway endpoint using an alias record.

D. Request a wildcard certificate that matches the custom domain name in AWS Certificate Manager (ACM) in the same Region. This step will allow you to secure your API with HTTPS using a certificate issued by ACM. A wildcard certificate means that it can match any subdomain under your domain name (such as *.example.com). You need to request or import a certificate in ACM that matches your custom domain name and verify that you own the domain name. You also need to request the certificate in the same Region as your API.

F. Create a custom domain name in API Gateway for the REST API. Import the certificate from AWS Certificate Manager (ACM). This step will allow you to associate your custom domain name with your API and use the certificate from ACM to enable HTTPS. You need to create a custom domain name in API Gateway for the REST API and specify the certificate ARN from ACM. You also need to create a base path mapping that maps a path from your custom domain name to your API stage.

References:

AWS Backup provides a centralized, fully managed solution to automate and manage backups for a wide range of AWS resources, including Aurora databases. Configuring a backup plan with daily backups and a 90-day retention policy ensures operational efficiency and simplicity for compliance purposes.

Option A: Aurora automated backups support point-in-time recovery but do not provide the operational efficiency of a centralized backup management system like AWS Backup.

Option B: AWS Backup with point-in-time recovery is a valid solution but does not specify daily backups with retention, which is the most operationally efficient approach.

Option C: Manual backups require significant operational overhead to create and delete backups regularly, which is not efficient.

Option D: Using AWS Backup with a daily backup plan and 90-day retention provides an automated and efficient solution.

AWS Documentation References:

AWS Backup Overview

Backing Up Aurora Databases

To resolve the issue of longer delivery times for meeting invitations, the solutions architect can recommend adding an Auto Scaling group for the application that sends meeting invitations and configuring the Auto Scaling group to scale based on the depth of the SQS queue. This will allow the application to scale up as the number of appointment requests increases, improving the performance and delivery times of the meeting invitations.

This answer is correct because it meets the requirements of migrating a 20 TB MySQL database within 2 weeks with minimal downtime and cost-effectively. The AWS Snowball Edge Storage Optimized device has up to 80 TB of usable storage space, which is enough to fit the database. The AWS Database Migration Service (AWS DMS) can migrate data from MySQL to Amazon Aurora, Amazon RDS for MySQL, or MySQL on Amazon EC2 with minimal downtime by continuously replicating changes from the source to the target. The AWS Schema Conversion Tool (AWS SCT) can convert the source schema and code to a format compatible with the target database. By using these services together, the company can migrate the database to AWS with minimal downtime and cost. The Snowball Edge device can be shipped back to AWS to finish the migration and continue the ongoing replication until the database is fully migrated.

References:

To use an SSL/TLS certificate that is issued by an external CA, the certificate must be imported to AWS Certificate Manager (ACM). ACM can send a notification when the certificate is nearing expiration, but it cannot automatically rotate the certificate. Therefore, the certificate must be rotated manually by importing a new certificate and applying it to the ALB.

References:

Importing Certificates into AWS Certificate Manager

Renewing and Rotating Imported Certificates

Using an ACM Certificate with an Application Load Balancer

Amazon RDS for Microsoft SQL Server is a fully managed service that offers SQL Server 2014, 2016, 2017, and 2019 editions while offloading database administration tasks such as backups, patching, and scaling. Amazon RDS supports read replicas, which are read-only copies of the primary database that can be used for reporting purposes without affecting the performance of the online application. This solution will meet the requirements with the least operational overhead, as it does not require any code changes or manual intervention.

References:

1 provides an overview of Amazon RDS for Microsoft SQL Server and its benefits.

2 explains how to create and use read replicas with Amazon RDS.

S3 Storage Lens is a cloud storage analytics feature that provides organization-wide visibility into object storage usage and activity across multiple AWS accounts in AWS Organizations. S3 Storage Lens can report the incomplete multipart upload object count as one of the metrics that it collects and displays on an interactive dashboard in the S3 console. S3 Storage Lens can also export metrics in CSV or Parquet format to an S3 bucket for further analysis. This solution will meet the requirements with the least operational overhead, as it does not require any code development or policy changes.

References:

1 explains how to use S3 Storage Lens to gain insights into S3 storage usage and activity.

2 describes the concept and benefits of multipart uploads.

File Gateway provides a seamless way to connect to the cloud in order to store application data files and backup images as durable objects in Amazon S3 cloud storage. File Gateway offers SMB or NFS-based access to data in Amazon S3 with local caching. It can be used for on-premises applications, and for Amazon EC2-based applications that need file protocol access to S3 object storage.

Volume Gateway presents cloud-backed iSCSI block storage volumes to your on-premises applications. Volume Gateway stores and manages on-premises data in Amazon S3 on your behalf and operates in either cache mode or stored mode. In the cached Volume Gateway mode, your primary data is stored in Amazon S3, while retaining your frequently accessed data locally in the cache for low latency access.

S3 Object Lock enables a write-once-read-many (WORM) model for objects stored in Amazon S3. It can help prevent objects from being deleted or overwritten for a fixed amount of time or indefinitely1. S3 Object Lock has two retention modes: governance mode and compliance mode. Compliance mode provides the highest level of protection and prevents any user, including the root user, from deleting or modifying an object version until the retention period expires. To use S3 Object Lock, a new bucket with Object Lock enabled must be created, and a default retention period can be optionally configured for objects placed in the bucket2. To bring existing objects into compliance, they must be recopied into the bucket with a retention period specified.

Option A is incorrect because S3 Versioning and S3 Lifecycle do not provide WORM protection for objects. Moreover, MFA delete only applies to deleting object versions, not modifying them.

Option B is incorrect because governance mode allows users with special permissions to override or remove the retention settings or delete the object if necessary. This does not meet the legal requirement of retaining all data for 7 years.

Option D is incorrect because S3 Batch Operations cannot be used to apply compliance mode retention periods to existing objects. S3 Batch Operations can only apply governance mode retention periods or legal holds. Reference URL: 2: S3/latest/userguide/object-lock-console.html 3: .com/AmazonS3/latest/userguide/storage-class-intro.html#sc-dynamic-data-access 4: 1: : : :

AWS DataSync is a data transfer service that makes it easy for you to move large amounts of data online between on-premises storage and AWS storage services over the internet or AWS Direct Connect. DataSync automatically encrypts your data in transit using TLS encryption, and verifies data integrity during transfer using checksums. DataSync can transfer data up to 10 times faster than open-source tools, and reduces operational overhead by simplifying and automating tasks such as scheduling, monitoring, and resuming transfers. References:

It uses Amazon Kinesis Data Firehose which is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3. This service requires less operational overhead as compared to option A, B, and D. Additionally, it also uses Amazon API Gateway which is a fully managed service for creating, deploying, and managing APIs. These services help in reducing the operational overhead and automating the data ingestion process.

AWS Lambda charges you based on the number of invocations and the execution time of your function. Since the data processing job is relatively small (2 MB of data), Lambda is a cost-effective choice. You only pay for the actual usage without the need to provision and maintain infrastructure.

C and E are the correct answers because they allow the company to create a public endpoint for its web application that supports session affinity (sticky sessions) and has a WAF applied for additional security. By creating a public Application Load Balancer, the company can distribute incoming traffic across multiple EC2 instances in an Auto Scaling group and specify the application target group. By creating a web ACL in AWS WAF and associating it with the Application Load Balancer, the company can protect its web application from common web exploits. By enabling session stickiness on the Application Load Balancer, the company can ensure that subsequent requests from a user during a session are routed to the same target. References:

Application Load Balancers

AWS WAF

Target Groups for Your Application Load Balancers

How Application Load Balancer Works with Sticky Sessions

e-to-process-data-as-it-is-being-retrieved-from-s3/

S3 Object Lambda is a new feature of Amazon S3 that enables customers to add their own code to process data retrieved from S3 before returning it to the application. By using S3 Object Lambda, the data can be processed and transformed in real-time, without the need to store multiple copies of the data in separate S3 buckets or DynamoDB tables.

In this case, the Pll can be removed from the data by the code added to S3 Object Lambda before returning the data to the two applications that do not need to process Pll. The one application that requires Pll can be pointed to the original S3 bucket where the Pll is still stored.

Using S3 Object Lambda is the simplest and most cost-effective solution, as it eliminates the need to maintain multiple copies of the same data in different buckets or tables, which can result in additional storage costs and operational overhead.

To store and access files that are 25 GB or larger across many EC2 instances and across multiple Availability Zones, Amazon Elastic File System (Amazon EFS) is a suitable solution. Amazon EFS provides a simple, scalable, elastic file system that can be mounted on multiple EC2 instances concurrently. Amazon EFS supports high availability and durability by storing data across multiple Availability Zones within a Region.

References:

What Is Amazon Elastic File System?

Using EFS with EC2

A gateway endpoint is a gateway that is a target for a specified route in your route table, used for traffic destined to a supported AWS service1. Amazon S3 does not support gateway endpoints, only interface endpoints2. Therefore, option A is incorrect.

An interface endpoint is an elastic network interface with a private IP address that serves as an entry point for traffic destined to a supported service1. An interface endpoint can provide secure access to Amazon S3 from within the Region, but not from the on-premises location. Therefore, option C is incorrect.

AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys to protect your data3. AWS KMS does not provide a way to access data on Amazon S3 without traversing the internet. Therefore, option D is incorrect.

AWS Transit Gateway is a service that enables you to connect your Amazon Virtual Private Clouds (VPCs) and your on-premises networks to a single gateway. You can create a gateway in AWS Transit Gateway to access Amazon S3 securely from both the Region and the on-premises location using AWS Direct Connect. Therefore, option B is correct.

it allows the solutions architect to review the S3 buckets to discover personally identifiable information (Pll) with the least operational overhead. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. Amazon Macie can analyze data in S3 buckets across multiple regions and provide insights into the type, location, and level of sensitivity of the data. References:

Amazon Macie

Analyzing data with Amazon Macie

This answer is correct because it meets the requirements of maximizing the reliability of the application’s infrastructure. You can update the DB instance to be Multi-AZ, which means that Amazon RDS automatically provisions and maintains a synchronous standby replica in a different Availability Zone. The primary DB instance is synchronously replicated across Availability Zones to a standby replica to provide data redundancy and minimize latency spikes during system backups. Running a DB instance with high availability can enhance availability during planned system maintenance. It can also help protect your databases against DB instance failure and Availability Zone disruption. You can also enable deletion protection on the DB instance, which prevents the DB instance from being deleted by any user. You can place the EC2 instances behind an Application Load Balancer, which distributes incoming application traffic across multiple targets, such as EC2 instances, in multiple Availability Zones. This increases the availability and fault tolerance of your applications. You can run the EC2 instances in an EC2 Auto Scaling group across multiple Availability Zones, which ensures that you have the correct number of EC2 instances available to handle the load for your application. You can use scaling policies to adjust the number of instances in your Auto Scaling group in response to changing demand.

References:

This approach allows users to upload files directly to S3 without passing through the application servers, reducing the load on the application and improving scalability. It leverages the client-side capabilities to handle the file uploads and offloads the processing to S3.

AWS Lambda supports container images as a packaging format for functions. You can use existing container development workflows to package and deploy Lambda functions as container images of up to 10 GB in size. You can also use familiar tools such as Docker CLI to build, test, and push your container images to Amazon Elastic Container Registry (Amazon ECR). You can then create an AWS Lambda function based on the container image of your job and configure Amazon EventBridge to invoke the function every 10 minutes using a cron expression. This solution will be cost-effective as you only pay for the compute time you consume when your function runs. References:

Amazon RDS Proxy is a fully managed database proxy that makes applications more scalable, more resilient to database failures, and more secure. RDS Proxy sits between your application and your relational database to pool and share established database connections, improving database efficiency and application scalability. RDS Proxy also reduces the load on the database by handling connection management and query retries for transient errors. By deploying RDS Proxy between your serverless application and Amazon RDS, you can avoid opening and closing connections to the database frequently, which can cause errors or run out of connections. This solution will also reduce operational costs and improve availability of your application. References:

s-to-amazon-ec2-linux-instances/

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda can be used to tag resources with the cost center ID of the user who created the resource, by querying the RDS database that maps users to cost centers. Amazon EventBridge is a serverless event bus service that enables event-driven architectures. EventBridge can be configured to react to AWS CloudTrail events, which are recorded API calls made by or on behalf of the AWS account. EventBridge can invoke the Lambda function when a resource is created in the specific AWS account, passing the user identity and resource information as parameters. This solution will meet the requirements, as it enables automatic tagging of resources based on the user and cost center mapping.

References:

1 provides an overview of AWS Lambda and its benefits.

2 provides an overview of Amazon EventBridge and its benefits.

3 explains the concept and benefits of AWS CloudTrail events.

AWS Lambda is a service that lets users run code without provisioning or managing servers. It automatically scales and manages the underlying compute resources for the code. It supports multiple languages, such as Java, Python, Node.js, and Go1. By using AWS Lambda for the REST API, the solution can meet the requirements of 1 GB of memory and minimal administrative effort.

Amazon RDS is a service that makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups. It supports multiple database engines, such as MySQL, PostgreSQL, Oracle, and SQL Server2. By using Amazon RDS for the data store, the solution can meet the requirements of 2 GB of storage and a relational format.

A. Amazon EC2. This solution will not meet the requirement of minimal administrative effort, as Amazon EC2 is a service that provides virtual servers in the cloud that users have to configure and manage themselves. It requires users to choose an instance type, an operating system, a security group, and other options3.

D. Amazon DynamoDB. This solution will not meet the requirement of a relational format, as Amazon DynamoDB is a service that provides a key-value and document database that delivers single-digit millisecond performance at any scale. It is a non-relational or NoSQL database that does not support joins or transactions.

E. Amazon Elastic Kubernetes Services (Amazon EKS). This solution will not meet the requirement of minimal administrative effort, as Amazon EKS is a service that provides a fully managed Kubernetes service that users have to configure and manage themselves. It requires users to create clusters, nodes groups, pods, services, and other Kubernetes resources.

Reference URL:

A cross-account IAM role is a way to grant users from one AWS account access to resources in another AWS account. The cross-account IAM role can have a read-only access policy attached to it, which allows the users to download objects from the S3 buckets without modifying or deleting them. The cross-account IAM role also reduces the operational overhead of managing multiple IAM users and policies in each account. The cross-account IAM role meets all the requirements of the question, while the other options do not. References:

it allows the solutions architect to create a separate organization for the research and development (R&D) business and move its AWS account to the new organization. By inviting the R&D AWS account to be part of the new organization after it has left the prior organization, the solutions architect can ensure that there is no overlap or conflict between the two organizations. The R&D AWS account can accept or decline the invitation to join the new organization. Once accepted, it will be subject to any policies and controls applied by the new organization. References:

Inviting an AWS Account to Join Your Organization

Leaving an Organization as a Member Account

This answer is correct because it meets the requirements of releasing the new version of APIs with minimal effects on customers and minimal data loss. A canary release deployment is a software development strategy in which a new version of an API is deployed for testing purposes, and the base version remains deployed as a production release for normal operations on the same stage. In a canary release deployment, total API traffic is separated at random into a production release and a canary release with a pre-configured ratio. Typically, the canary release receives a small percentage of API traffic and the production release takes up the rest. The updated API features are only visible to API traffic through the canary. You can adjust the canary traffic percentage to optimize test coverage or performance. By keeping canary traffic small and the selection random, most users are not adversely affected at any time by potential bugs in the new version, and no single user is adversely affected all the time. After the test metrics pass your requirements, you can promote the canary release to the production release and disable the canary from the deployment. This makes the new features available in the production stage.

References:

it allows the company to encrypt the Kubernetes secrets object in the EKS cluster with the least operational overhead. By enabling secrets encryption in the EKS cluster, the company can use AWS Key Management Service (AWS KMS) to generate and manage encryption keys for encrypting and decrypting secrets at rest. This is a simple and secure way to protect sensitive information in EKS clusters. References:

Encrypting Kubernetes secrets with AWS KMS

Kubernetes Secrets

AWS ParallelCluster is a service that allows you to create and manage high-performance computing (HPC) clusters on AWS. It supports multiple schedulers, including AWS Batch, which can run distributed workloads across multiple EC2 instances1.

MPI is a standard for message passing between processes in parallel computing. It provides functions for sending and receiving data, synchronizing processes, and managing communication groups2.

By using AWS ParallelCluster and MPI libraries, you can take advantage of the following benefits:

You can easily create and configure HPC clusters that meet your specific requirements, such as instance type, number of nodes, network configuration, and storage options1.

You can leverage the scalability and elasticity of AWS to run large-scale parallel workloads without worrying about provisioning or managing servers1.

You can use MPI libraries to optimize the performance and efficiency of your parallel applications by enabling inter-process communication and data exchange2.

You can choose from a variety of MPI implementations that are compatible with AWS ParallelCluster, such as Open MPI, Intel MPI, and MPICH3.

Kubernetes API requests within your cluster's VPC (such as node to control plane communication) use the private VPC endpoint.

EBS snapshots are point-in-time backups of EBS volumes that can be used to restore data or create new volumes. EBS snapshots can be locked to prevent accidental deletion using a feature called EBS Snapshot Lock. When a snapshot is locked, it cannot be deleted by any user, including the root user, until it is unlocked. The lock policy can also specify a retention period, after which the snapshot can be deleted. This solution will meet the requirements with the least administrative effort, as it does not require any code development or policy changes.

References:

1 explains how to lock and unlock EBS snapshots using EBS Snapshot Lock.

2 describes the concept and benefits of EBS snapshots.

This answer is correct because it meets the requirements of accommodating the larger workload without adding infrastructure and minimizing the cost. Reserved DB instances are a billing discount applied to the use of certain on-demand DB instances in your account. Reserved DB instances provide you with a significant discount compared to on-demand DB instance pricing. You can buy reserved DB instances for the total workload and choose between three payment options: No Upfront, Partial Upfront, or All Upfront. You can make the Amazon RDS for PostgreSQL DB instance larger by modifying its instance type to a higher performance class. This way, you can increase the CPU, memory, and network capacity of your DB instance and handle the increased workload.

References:

To store and retrieve reports that are frequently accessed during the first week and must be stored for several years, S3 Standard and S3 Glacier are suitable solutions. S3 Standard offers high durability, availability, and performance for frequently accessed data. S3 Glacier offers secure and durable storage for long-term data archiving at a low cost. S3 Lifecycle rules can be used to transition the reports from S3 Standard to S3 Glacier after 7 days, which can reduce storage costs. S3 Glacier also supports retrieval within 6 hours.

References:

Storage Classes

Object Lifecycle Management

Retrieving Archived Objects from Amazon S3 Glacier

aws.amazon.com/efs/latest/ug/transfer-data-to-efs.html

Using RDS Proxy, you can handle unpredictable surges in database traffic. Otherwise, these surges might cause issues due to oversubscribing connections or creating new connections at a fast rate. RDS Proxy establishes a database connection pool and reuses connections in this pool. This approach avoids the memory and CPU overhead of opening a new database connection each time. To protect the database against oversubscription, you can control the number of database connections that are created.

B and E are the correct answers because they allow the solutions architect to ensure that API calls to Amazon DynamoDB from Amazon EC2 instances in a VPC do not travel across the internet. By creating a gateway endpoint for DynamoDB, the solutions architect can enable private connectivity between the VPC and DynamoDB. By creating a security group entry in the endpoint’s security group to provide access, the solutions architect can control which EC2 instances can communicate with DynamoDB through the endpoint. References:

Gateway Endpoints

Controlling Access to Services with VPC Endpoints

An IAM policy is a document that defines the permissions for an IAM identity (such as a user, group, or role). You can use IAM policies to grant permissions to existing users and groups based on department. You can create an IAM policy that grants least privilege permission, which means that you only grant the minimum permissions required for the users to perform their tasks. You can then attach the policy to the IAM groups, which will apply the policy to all the users in those groups. This solution will reduce operational costs and simplify configuration and management of permissions. References:

An Auto Scaling group is a collection of EC2 instances that share similar characteristics and can be scaled in or out automatically based on demand. An Auto Scaling group can be placed behind an Application Load Balancer, which is a type of Elastic Load Balancing load balancer that distributes incoming traffic across multiple targets in multiple Availability Zones. This solution will improve the resiliency of the web tier by providing high availability, scalability, and fault tolerance. An Amazon RDS Multi-AZ deployment is a configuration that automatically creates a primary database instance and synchronously replicates the data to a standby instance in a different Availability Zone. When a failure occurs, Amazon RDS automatically fails over to the standby instance without manual intervention. This solution will improve the resiliency of the database tier by providing data redundancy, backup support, and availability. This combination of steps will meet the requirements with minimal changes to the application during the migration.

References:

1 describes the concept and benefits of an Auto Scaling group.

2 provides an overview of Application Load Balancers and their benefits.

3 explains how Amazon RDS Multi-AZ deployments work and their benefits.

The number of instances in your Auto Scaling group can be driven by how long it takes to process a message and the acceptable amount of latency (queue delay). The solution is to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda scales automatically based on the incoming requests, but it may take some time to initialize new instances of your function if there is a sudden increase in demand. This may result in high latency or cold starts for your application. To avoid this, you can use provisioned concurrency, which ensures that your function is initialized and ready to respond at any time. You can also set up a scheduled scaling policy that increases the provisioned concurrency before employees begin to use the application each day, and decreases it when the demand is low. References:

AWS Fargate is a technology that you can use with Amazon ECS to run containers without having to manage servers or clusters of Amazon EC2 instances. With Fargate, you no longer have to provision, configure, or scale clusters of virtual machines to run containers.

Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon Relational Database Service (RDS) that makes applications more scalable, more resilient to database failures, and more secure1. RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability2. RDS Proxy also reduces failover times for Aurora and RDS databases by up to 66% and enables IAM authentication and Secrets Manager integration for database access1. RDS Proxy can be enabled for most applications with no code changes2.

AWS Backup is a fully managed service that enables users to centralize and automate the backup of data across AWS services. It can create and manage backup plans that specify the frequency and retention period of backups. It can also assign backup resources to backup vaults, which are containers that store backup data1. By using AWS Backup, the solution can ensure that the RDS backups are consistent, restorable, and retained for a minimum period of 2 years.

B. Configure a backup window for the RDS DB instances for daily snapshots. Assign a snapshot retention policy of 2 years to each RDS DB instance. Use Amazon Data Lifecycle Manager (Amazon DLM) to schedule snapshot deletions. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as Amazon DLM is not compatible with RDS snapshots and cannot be used to schedule snapshot deletions2.

C. Configure database transaction logs to be automatically backed up to Amazon CloudWatch Logs with an expiration period of 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as database transaction logs are not sufficient to restore a database to a point in time. They only capture the changes made to the database, not the full state of the database3.

D. Configure an AWS Database Migration Service (AWS DMS) replication task. Deploy a replication instance, and configure a change data capture (CDC) task to stream database changes to Amazon S3 as the target. Configure S3 Lifecycle policies to delete the snapshots after 2 years. This solution will not meet the requirement of ensuring that the backups are consistent and restorable, as AWS DMS is a service that helps users migrate databases to AWS, not back up databases. It also requires additional resources and configuration, such as replication instances and CDC tasks.

Reference URL:

it allows the company to migrate the on-premises database to a managed AWS service that supports auto scaling capabilities and has the least administrative overhead. Amazon Aurora Serverless v2 is a configuration of Amazon Aurora that automatically scales compute capacity based on workload demand. It can scale from hundreds to hundreds of thousands of transactions in a fraction of a second. Amazon Aurora Serverless v2 also supports MySQL-compatible databases and AWS Direct Connect connectivity. References:

Amazon Aurora Serverless v2

Connecting to an Amazon Aurora DB Cluster

AWS uses the existing infrastructure of a VPC to create a VPC peering connection; it is neither a gateway nor a VPN connection, and does not rely on a separate piece of physical hardware. There is no single point of failure for communication or a bandwidth bottleneck.

Amazon Neptune: Neptune is a fully managed graph database service that is optimized for storing and querying highly connected data. It supports both property graph and RDF graph models, making it suitable for applications that need to analyze relationships between data entities.

Neptune Streams: Neptune Streams captures changes to the graph and streams these changes to other AWS services. This is useful for applications that need to monitor and respond to changes in real-time, such as providing recommendations based on user interactions and relationships.

Least Operational Overhead: Using Neptune Streams directly with Amazon Neptune ensures that the solution is tightly integrated, reducing the need for additional components and minimizing operational overhead. This integration simplifies the architecture by eliminating the need for a separate service like Kinesis for change processing.

References:

Amazon Neptune Documentation

Neptune Streams Documentation

Option B: A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D: Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A: Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C: Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E: A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

Target Tracking Scaling Policy: Automatically adjusts the number of EC2 instances to meet traffic demand, ensuring cost optimization without over-provisioning.

EFS Volume: Allows shared access to data, which is crucial for dynamic scaling environments.

A. EC2 with EBS: Does not support SQL Server Always On availability groups effectively.

B. RDS Multi-AZ: Provides high availability but does not support SQL Server Always On availability groups.

C. EC2 with FSx for Windows: Best solution for SQL Server Always On as FSx provides shared storage compatible with SQL Server clustering.

D. EC2 with S3: S3 is not suitable for SQL Server storage.

References: Amazon FSx for Windows

A. ElastiCache: Provides in-memory caching, not suitable for persistent, scalable databases.

B. DynamoDB Streams + Lambda: Manages replication manually, increasing latency and operational complexity.

C. Aurora Global Database: Provides high availability but does not support active-active configuration.

D. DynamoDB Global Tables: Provides active-active configuration and sub-second RPO.

References: Amazon DynamoDB Global Tables

Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

For improving availability and performance of the hybrid application, the following solutions are optimal:

AWS Global Accelerator (Option A): Global Accelerator provides high availability and improves performance by using the AWS global network to route user traffic to the nearest healthy endpoint (across AWS Regions). By adding the Network Load Balancers as endpoints, Global Accelerator ensures that traffic is routed efficiently to the closest endpoint, improving both availability and performance.

Network Load Balancer (Option D): The stateful TCP-based workload hosted on Amazon EC2 instances and the stateless UDP-based workload hosted on-premises are best served by Network Load Balancers (NLBs). NLBs are designed to handle TCP and UDP traffic with ultra-low latency and can route traffic to both EC2 and on-premises endpoints.

Option B (CloudFront and Route 53): CloudFront is better suited for HTTP/HTTPS workloads, not for TCP/UDP-based applications.

Option C (ALB): Application Load Balancers do not support the stateless UDP-based workload, making NLBs the better choice for both TCP and UDP.

AWS References:

AWS Global Accelerator

Network Load Balancer

Amazon S3 Intelligent-Tiering is the most cost-effective solution for this scenario, providing both high availability and durability while adjusting automatically to changing access patterns. It moves data across two access tiers: one optimized for frequent access and another for infrequent access, based on usage patterns. This tiering ensures that the company avoids paying for unused storage while also keeping frequently accessed data in a more accessible tier.

Key AWS references and benefits of S3 Intelligent-Tiering:

High Durability and Availability: Amazon S3 offers 99.999999999% durability and 99.9% availability for objects stored, ensuring data is always protected.

Automatic Tiering: Data is automatically moved between tiers based on access patterns, making it ideal for workloads with unpredictable or variable access patterns.

No Retrieval Fees: Unlike S3 One Zone-IA or Glacier, there are no retrieval fees, making this more cost-effective in scenarios where access patterns vary over time.

AWS Documentation: According to the AWS Well-Architected Framework under the Cost Optimization Pillar, S3 Intelligent-Tiering is recommended for storage when access patterns change over time, as it minimizes costs while maintaining availability​.

Detailed Explanation:

A. IAM identity provider: Does not support centralized management across multiple accounts.

B. AWS Managed AD: Unnecessary if an on-premises Active Directory already exists.

C. IAM Identity Center + Existing AD: Best approach to integrate existing Active Directory for SSO, with MFA and centralized permissions.

D. Lambda for synchronization: Adds complexity and does not leverage IAM Identity Center capabilities.

References: AWS IAM Identity Center

To analyze the performance of the web application with granularity of no more than 2 minutes, enabling detailed monitoring on EC2 instances is the best solution. By default, CloudWatch provides metrics at a 5-minute interval. Enabling detailed monitoring allows you to collect metrics at 1-minute intervals, which will give you the level of granularity you need to analyze performance during peak traffic.

Amazon CloudWatch metrics can then be used to analyze CPU utilization, memory usage, disk I/O, and network throughput, among other performance-related metrics, at the desired granularity.

Option A: Sending CloudWatch logs to Redshift for analysis is unnecessary and overcomplicated for simple performance analysis, which can be done using CloudWatch metrics alone.

Option C: Fetching EC2 logs via Lambda adds complexity, and CloudWatch metrics already provide the required data for performance analysis.

Option D: Sending logs to S3 and using Redshift for analysis is also more complex than necessary for simple performance monitoring.

AWS References:

Monitoring Amazon EC2 with CloudWatch

Amazon CloudWatch Detailed Monitoring

To meet the requirements for tagging and preventing instance creation or deletion without proper tags, the company can use a combination of AWS Organizations tag policies and service control policies (SCPs).

Tag Policies: These enforce specific tag values across resources. Creating a tag policy with required values (e.g., sensitive, non-sensitive) and attaching it to the appropriate organizational unit (OU) ensures consistency in tagging.

SCPs: SCPs can be used to enforce compliance by preventing instance creation without a tag and preventing tag deletion. These policies control actions at the account level across the organization.

Key AWS features:

Tag Policies help standardize tags across accounts, and SCPs enforce governance by restricting actions that violate the policies.

AWS Documentation: AWS best practices recommend using tag policies and SCPs to enforce compliance across multiple accounts within AWS Organizations​.

The combination of these solutions provides an efficient and automated way to migrate data while preserving metadata and ensuring cleanup:

Create a new S3 bucket with versioning enabled (Option A) to preserve object metadata like version IDs during migration.

Use S3 batch operations (Option B) to efficiently copy data from specific prefixes in the source bucket to the destination bucket, ensuring minimal overhead.

Use an S3 Lifecycle policy (Option F) to automatically delete the data from the source bucket after it has been migrated, reducing manual intervention.

Option C (CopyObject API): This approach would require more manual scripting and effort.

Option D (Same-Region Replication): SRR is designed for ongoing replication, not for one-time migrations.

Option E (DataSync): DataSync adds more complexity than necessary for this task.

AWS References:

S3 Batch Operations

S3 Lifecycle Policies

The best solution is to use Amazon SQS to receive updates from the mobile app and process them with an AWS Lambda function. The Lambda function can rewrite the message body as necessary for each shipper and then publish the updates to the appropriate SNS topic for distribution. This setup ensures that each shipper receives only the relevant data and minimizes operational overhead by using managed services.

Option A (SNS filter policy): SNS does not have the capability to rewrite message bodies before forwarding.

Option C (Second SNS topic): Using an additional SNS topic adds unnecessary complexity without solving the message rewriting requirement.

Option D (EventBridge Pipes): EventBridge Pipes is more complex than necessary for this use case, and Lambda can handle the logic more efficiently.

AWS References:

Amazon SQS

Amazon SNS with Lambda

To meet the requirement of controlling key rotation with minimal operational overhead, creating a customer managed key (CMK) in AWS KMS is the optimal solution. With CMKs, you can define custom key rotation policies, ensuring that you retain control over the key lifecycle, including enabling automatic key rotation every year.

Key AWS features:

Custom Key Management: A customer managed key allows you to control the key policies, lifecycle, and enable key rotation for compliance.

Least Operational Overhead: Using a customer managed key simplifies encryption management while offering more flexibility than AWS managed or owned keys.

AWS Documentation: The AWS Well-Architected Framework recommends customer managed keys for environments where key control and flexibility are required​.

Why Option B is Correct:

Amazon SQS: Ensures no requests are lost, even during traffic spikes.

API Gateway: Handles dynamic traffic patterns efficiently, integrating with SQS for asynchronous processing.

Lambda: Polls the queue and processes requests in a serverless and scalable manner.

Dead-Letter Queue (DLQ): Ensures failed messages are retried or logged for debugging.

Why Other Options Are Not Ideal:

Option A: Elastic Beanstalk cannot handle queue-based decoupling, making it unsuitable for spiky traffic.

Option C: ALB to Lambda does not provide buffering for traffic spikes, risking request loss.

Option D: SNS is better suited for notifications, not reliable for ensuring message durability.

AWS References:

Amazon SQS:AWS Documentation - SQS

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet. Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

References:

VPC Endpoints

Amazon SQS and VPC Endpoints

Amazon API Gateway provides a scalable and reusable solution for interacting with DynamoDB without requiring direct access by developers. By setting up a REST API with a POST method that integrates with DynamoDB's PutItem action, developers can submit data (such as user ratings) to the DynamoDB table through API Gateway, without having to directly interact with the database. This solution is serverless and minimizes operational overhead.

Option A: Using ALB with Lambda adds complexity and is less efficient for this use case.

Option B: While using Lambda is possible, API Gateway provides a more scalable, reusable interface.

Option C: SQS with Lambda introduces unnecessary components for a simple put operation.

AWS References:

Amazon API Gateway with DynamoDB

Why Option C is Correct:

S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.

Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.

Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.

Why Other Options Are Not Ideal:

Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.

Option B: Encryption ensures data security but does not address access management.

Option D: Pre-signed URLs are temporary and not suitable for managing access at scale.

AWS References:

Amazon S3 Access Points:AWS Documentation - S3 Access Points

Option A combines ECS with Fargate for scalability, RDS for relational data, and SQS for decoupling with message ordering (FIFO queues).

Option B uses SNS, which does not maintain message order.

Option C is suitable for serverless workflows but not relational data.

Option D relies on Elastic Beanstalk, which offers less flexibility for scaling.

Compute Savings Plans offer the most flexible and cost-effective solution for the production instances, as they provide significant savings (up to 66%) for both EC2 and AWS Lambda usage, while allowing flexibility in the type of instance family, size, and even region. For nonproduction instances, using On-Demand Instances ensures you only pay for the instances when they are running, and shutting them down during off-hours further optimizes cost.

Key AWS features:

Compute Savings Plans: Provide savings based on consistent usage, making it ideal for production environments with steady load.

On-Demand Instances: Suitable for nonproduction environments that are used intermittently. Shutting them down when not in use avoids unnecessary costs.

AWS Documentation: According to AWS's cost optimization best practices, using a combination of Savings Plans for production and On-Demand Instances for nonproduction environments that are used sparingly results in optimal cost savings​​.

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

References:

Amazon FSx for NetApp ONTAP

NetApp SnapMirror

Using cross-account IAM roles is the most secure and scalable way to share data between AWS accounts.

A trust relationship allows the analytics team's account to assume the role in the main account and access the DynamoDB table directly.

A is feasible but involves data duplication and additional costs for storing the JSON files in S3.

B and C violate security best practices by allowing public access to sensitive data and sharing credentials, which is highly discouraged.

AWS Documentation Reference:

Cross-Account Access with Roles

Best Practices for Amazon DynamoDB Security

Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handle Apache Parquet files efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.

Key AWS features:

Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost-effective solution compared to Redshift or EMR for occasional queries.

Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.

AWS Documentation: Athena's compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity​​.

Understanding the Requirement: The company needs to transfer 50 TB of data to AWS with minimal operational overhead and no available network bandwidth for the transfer. The transformation job must continue running in the AWS Cloud.

Analysis of Options:

AWS DataSync and AWS Glue: DataSync is suitable for online data transfer, but there is no available network bandwidth. AWS Glue can be used for data transformation but does not solve the bandwidth issue.

AWS Snowcone: Snowcone is a smaller device suitable for smaller data transfers, and deploying the transformation application on it may not be feasible for 50 TB of data.

AWS Snowball Edge Storage Optimized with Glue: This device is designed for large data transfers. Copying the data to the device is straightforward, and AWS Glue can handle data transformation in the cloud.

AWS Snowball Edge Storage Optimized with EC2: This involves setting up EC2 instances for transformation, adding operational complexity compared to using AWS Glue.

Best Solution:

AWS Snowball Edge Storage Optimized with AWS Glue: This provides the least operational overhead for transferring large amounts of data and setting up the transformation job in the cloud.

References:

AWS Snowball Edge

AWS Glue

The most secure solution for allowing EC2 instances to access an S3 bucket is by using IAM roles. An IAM role can be created with an access policy that grants the required permissions (e.g., to read and write to the S3 bucket). The IAM role is then associated with the EC2 instances through an IAM instance profile.

By associating the role with the instances, the EC2 instances can securely assume the role and receive temporary credentials via the instance metadata service. This avoids the need to store credentials (such as access keys) on the instances or within the application, enhancing security and reducing the risk of credentials being exposed.

AWS CloudFormation can be used to automate the creation of the entire infrastructure, including EC2 instances, IAM roles, and associated policies.

AWS References:

IAM Roles for EC2 Instances outlines the use of IAM roles for secure access to AWS services.

AWS CloudFormation User Guide details how to create and manage resources using CloudFormation templates.

Why the other options are incorrect:

A. Save IAM access key in UserData: This is insecure because it involves storing long-term credentials in the instance user data, which can be exposed.

B. Store access keys in S3: This is also insecure, as it involves managing and distributing long-term credentials, which should be avoided.

D. Retrieve access keys via a script: This approach is unnecessarily complex and less secure than using IAM roles, which provide temporary credentials automatically.

Amazon Route 53 Multivalue Answer Routing: This routing policy allows Route 53 to return multiple values, such as IP addresses, in response to DNS queries. This can distribute traffic across multiple resources and includes health checks to ensure traffic is only routed to healthy instances.

Health Checks:

Configure health checks for each Region to monitor the health of the website instances.

Route 53 will only include healthy instances in the DNS responses, ensuring that traffic is not routed to an unhealthy Region.

Load Distribution and Disaster Recovery:

Multivalue answer routing helps balance the load between instances in different Regions.

If instances in one Region become unhealthy, Route 53 will route traffic to the healthy instances in the other Region.

Operational Simplicity: This solution does not require complex configurations or additional resources, making it a simple yet effective way to distribute traffic and ensure high availability.

References:

Amazon Route 53 Routing Policies

Multivalue Answer Routing

AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. By creating a Config rule, you can automatically check whether your Amazon EBS volumes are encrypted and flag those that are not, with minimal cost and configuration effort.

AWS Config Rule: AWS Config provides managed rules that you can use to automatically check the compliance of your resources against predefined or custom criteria. In this case, you would create a rule to evaluate EBS volumes and determine if they are encrypted. If a volume is not encrypted, the rule will flag it, allowing you to take corrective action.

Operational Overhead: This approach significantly reduces operational overhead because once the rule is in place, it continuously monitors your EBS volumes for compliance, and there’s no need for manual checks or custom scripting.

Why Not Other Options?:

Option A (Lambda with API calls and EventBridge): While this can work, it involves writing and maintaining custom code, which increases operational overhead compared to using a managed AWS Config rule.

Option B (API calls on Fargate): Running API calls on Fargate is more complex and costly compared to using AWS Config, which provides a simpler, managed solution.

Option C (IAM policy with Cost Explorer): This option does not directly enforce encryption compliance and involves manual intervention, making it less efficient and more prone to errors.

AWS References:

AWS Config Rules - Overview of AWS Config rules and how they can be used to evaluate resource configurations.

Amazon EBS Encryption - Information on how to manage and enforce encryption for EBS volumes.

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

References:

AWS Savings Plans

EC2 Instance Types

Option B: Pre-signed URLs provide temporary, authenticated access to S3, limiting uploads to the time frame specified. This solution is lightweight, efficient, and easy to implement.

Option A requires the management of IAM temporary credentials, adding complexity.

Option C involves unnecessary development effort.

Option D introduces more complexity with STS and roles than pre-signed URLs.

AWS Storage Gateway Tape Gateway provides a seamless way to migrate backup data to AWS without requiring changes to the backup software. Migrating to S3 Glacier Deep Archive ensures long-term, cost-effective storage for data that rarely needs retrieval.

Option A: S3 Standard-IA is more expensive than Glacier for long-term storage.

Option B and D: Glacier Flexible Retrieval is costlier than Glacier Deep Archive for archival use cases with low retrieval frequency.

AWS Documentation References:

AWS Storage Gateway Tape Gateway

S3 Glacier Storage Classes

EBS Encryption:

Default EBS Encryption: Can be enabled for new EBS volumes.

Use of AWS KMS: Specify AWS KMS keys to handle encryption and decryption of data transparently.

Amazon RDS Encryption:

RDS Encryption: Encrypts the underlying storage for RDS instances using AWS KMS.

Configuration: Enable encryption when creating the RDS instance or modify an existing instance to enable encryption.

Least Amount of Changes:

Both EBS and RDS support seamless encryption with AWS KMS, requiring minimal changes to the existing infrastructure.

Enables compliance with regulatory requirements without modifying the application.

Operational Efficiency: Using AWS KMS for both EBS and RDS ensures a consistent, managed approach to encryption, simplifying key management and enhancing security.

References:

Amazon EBS Encryption

Amazon RDS Encryption

AWS Key Management Service

AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

References:

Amazon EC2 Auto Scaling

High Availability for Amazon EC2

Amazon API Gateway with CloudFront: API Gateway allows you to create, deploy, and manage APIs, while CloudFront provides a CDN to deliver content with low latency and high transfer speeds.

AWS WAF (Web Application Firewall):

AWS WAF can be configured in CloudFront to protect against common web exploits, including SQL injection and cross-site scripting (XSS).

WAF allows you to create custom rules to block specific attack patterns and can be managed centrally.

Configuration:

Deploy your APIs using Amazon API Gateway.

Set up an Amazon CloudFront distribution in front of the API Gateway.

Configure AWS WAF on the CloudFront distribution to apply security rules.

Operational Efficiency: This solution provides robust protection with minimal operational overhead by leveraging managed AWS services, ensuring that your APIs are secure without extensive custom implementation.

References:

Using AWS WAF to Protect Your APIs

How CloudFront Works with AWS WAF

The company needs a cloud-based storage solution for frequently accessed data with low latency, while retaining their current on-premises infrastructure for some data storage. AWS Storage Gateway's Volume Gateway with cached volumes is the most appropriate solution for this scenario.

AWS Storage Gateway - Volume Gateway (Cached Volumes):

Volume Gateway with cached volumes allows you to store frequently accessed data in the AWS Cloud while keeping the most recently accessed data cached locally on-premises. This ensures low-latency access to active data while providing scalability for the rest of the data in the cloud.

The cached volume option stores the primary data in Amazon S3 but caches frequently accessed data locally, ensuring fast access. This configuration is well-suited for applications that require fast access to frequently used data but can tolerate cloud-based storage for the rest.

Since the company is facing limited on-premises storage, cached volumes provide an ideal solution, as they reduce the need for additional on-premises storage infrastructure.

Why Not the Other Options?:

Option A (S3 File Gateway): S3 File Gateway provides a file-based interface (SMB/NFS) for storing data directly in S3. While it is great for file storage, the company’s need for block-level storage with iSCSI targets makes Volume Gateway a better fit.

Option C (Volume Gateway - Stored Volumes): Stored volumes keep all the data on-premises and asynchronously back up to AWS. This would not address the company's storage limitations since they would still need substantial on-premises storage.

Option D (Tape Gateway): Tape Gateway is designed for archiving and backup, not for frequently accessed low-latency data.

AWS References:

AWS Storage Gateway - Volume Gateway

Amazon EFS with Max I/O performance mode is designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn't support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

Detailed Explanation:

A. EventBridge Rule: Detects modifications to CloudFront distributions in real time and triggers the Lambda function for further action.

B. ALB + Config: Focuses on ALB security violations, not relevant for CloudFront logging changes.

C. Lambda + SNS: Provides real-time notifications about changes in logging configuration.

D. GuardDuty: Focuses on threat detection, not logging configuration changes.

E. API Gateway + WAF: Unrelated to CloudFront logging changes.

References: Amazon EventBridge, Amazon SNS

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

References:

Amazon Kinesis Data Firehose

AWS Fargate

Amazon CloudFront Price Class: Limiting edge locations through price classes (Price Class 100, 200, or All) minimizes costs by only using edge locations in specific geographic areas.

Optimal Region Selection: Storing files in a single region (e.g., us-east-2) with CloudFront distribution reduces costs compared to storing data in multiple S3 regions.

Edge Location Considerations: Limiting edge locations for the United States and Europe ensures cost efficiency while maintaining acceptable performance.

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

References:

Amazon S3 Intelligent-Tiering

S3 Lifecycle Policies

The Gateway Load Balancer (GWLB) is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts. GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).

Key AWS features:

Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.

Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.

AWS Documentation: The use of GWLB aligns with AWS’s best practices for centralized network security, simplifying architecture and reducing operational complexity​.

A. Kinesis Data Streams: Supports high throughput, strict ordering, multiple consumers, and data retention for 365 days.

B. SNS + SQS FIFO: Can enforce ordering but lacks native support for 500 KB messages and retention requirements.

C. EventBridge: Lacks strict ordering and message size compatibility.

D. SNS + Firehose: Not designed for strict ordering or large message sizes.

References: Amazon Kinesis Data Streams

Amazon CloudWatch Container Insights is designed for monitoring containerized environments like EKS. It provides native support for collecting and visualizing metrics and logs in a centralized location through CloudWatch dashboards, offering the most operationally efficient solution.

Option A: Using the CloudWatch agent provides basic metrics but lacks the specific insights required for containerized applications.

Option B: Kinesis Data Streams and Firehose add unnecessary complexity for this use case.

Option C: CloudTrail is for auditing API activity and is not designed for application metrics and log aggregation.

AWS Documentation References:

Amazon CloudWatch Container Insights

Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

References:

Placement Groups

High Performance Computing on AWS

Understanding the Requirement: The company needs to enable communication between VPCs across multiple AWS Regions with minimal administrative effort.

Analysis of Options:

VPC Peering: Managing multiple VPC peering connections across regions is complex and difficult to scale, leading to significant administrative overhead.

AWS Direct Connect Gateways: Primarily used for creating private connections between AWS and on-premises environments, not for inter-VPC communication across regions.

AWS Transit Gateway: Simplifies VPC interconnections within a region and supports Transit Gateway peering for cross-region connectivity, reducing administrative complexity.

AWS PrivateLink: Used for accessing AWS services and third-party services over a private connection, not for inter-VPC communication.

Best Solution:

AWS Transit Gateway with Transit Gateway Peering: This option provides a scalable and efficient solution for managing VPC communications both within a single region and across multiple regions with minimal administrative overhead.

References:

AWS Transit Gateway

Transit Gateway Peering

A. Amazon EFS: Provides a scalable, shared file storage solution with minimal operational overhead. It's ideal for Linux-based workloads.

B. Amazon FSx for NetApp ONTAP: More suited for workloads requiring NetApp-specific features.

C. Amazon EBS: Requires manual management of file shares, which increases operational overhead.

D. Amazon FSx for Windows File Server: Best suited for Windows SMB workloads with low operational overhead.

E. Amazon FSx for OpenZFS: Better for Linux and Unix-based workloads.

References: Amazon EFS, Amazon FSx

Understanding the Requirement: The application needs to write to multiple block storage volumes in multiple EC2 Nitro-based instances simultaneously to achieve higher availability.

Analysis of Options:

General Purpose SSD (gp3) with Multi-Attach: Supports Multi-Attach but does not provide the highest performance required for critical applications.

Throughput Optimized HDD (st1) with Multi-Attach: Not suitable for applications requiring high performance and low latency.

Provisioned IOPS SSD (io2) with Multi-Attach: Provides high performance and durability, suitable for applications requiring simultaneous writes and high availability.

General Purpose SSD (gp2) with Multi-Attach: Similar to gp3 but with less flexibility and performance.

Best Solution:

Provisioned IOPS SSD (io2) with Multi-Attach: This solution ensures the highest performance and availability for the application by allowing multiple EC2 instances to attach to and write to the same EBS volume simultaneously.

References:

Amazon EBS Multi-Attach

Provisioned IOPS SSD (io2)

To make all the data available to various teams and minimize operational overhead, the company can create a data lake by using AWS Lake Formation. This will allow the company to centralize all the data in one place and use fine-grained access controls to manage access to the data. To meet the requirements of the company, the solutions architect can create a data lake by using AWS Lake Formation, create an AWS Glue JDBC connection to Amazon RDS, and register the S3 bucket in Lake Formation. The solutions architect can then use Lake Formation access controls to limit access to the data. This solution will provide the ability to manage fine-grained permissions for the data and minimize operational overhead.

#:~:text=If%20you%20are%20a%20developer%20who%20needs%20to%20digitally,a%20broad%20set%20of%20industry%20and%20regional%20compliance%20regimes.

This solution meets the requirements of a disaster recovery solution to back up the data that is generated by an analytics application, stored in JSON format, and must be accessible in milliseconds if it is needed. Amazon S3 Standard is a durable and scalable storage class for frequently accessed data. It can store any amount of data and provide high availability and performance. It can also support millisecond access time for data retrieval.

Option A is incorrect because Amazon OpenSearch Service (Amazon Elasticsearch Service) is a search and analytics service that can index and query data, but it is not a backup solution for data stored in JSON format. Option B is incorrect because Amazon S3 Glacier is a low-cost storage class for data archiving and long-term backup, but it does not support millisecond access time for data retrieval. Option D is incorrect because Amazon RDS for PostgreSQL is a relational database service that can store and query structured data, but it is not a backup solution for data stored in JSON format.

References:

To meet the new requirement of transferring files over a private route, the EC2 instances should be moved to private subnets, which do not have direct access to the internet. This ensures that the traffic for file transfers does not go over the internet. To enable the EC2 instances to access Amazon S3, a VPC endpoint for Amazon S3 can be created. VPC endpoints allow resources within a VPC to communicate with resources in other services without the traffic being sent over the internet. By linking the VPC endpoint to the route table for the private subnets, the EC2 instances can access Amazon S3 over a private connection within the VPC.

You can use CloudFront to deliver video on demand (VOD) or live streaming video using any HTTP origin. One way you can set up video workflows in the cloud is by using CloudFront together with AWS Media Services. azon.com/AmazonCloudFront/latest/DeveloperGuide/on-demand-streaming-video.html

Using AWS WAF has several benefits. Additional protection against web attacks using criteria that you specify. You can define criteria using characteristics of web requests such as the following: Presence of SQL code that is likely to be malicious (known as SQL injection). Presence of a script that is likely to be malicious (known as cross-site scripting). AWS Firewall Manager simplifies your administration and maintenance tasks across multiple accounts and resources for a variety of protections.

In compliance mode, a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account. When an object is locked in compliance mode, its retention mode can't be changed, and its retention period can't be shortened. Compliance mode helps ensure that an object version can't be overwritten or deleted for the duration of the retention period. In governance mode, users can't overwrite or delete an object version or alter its lock settings unless they have special permissions. With governance mode, you protect objects against being deleted by most users, but you can still grant some users permission to alter the retention settings or delete the object if necessary. In Governance mode, Objects can be deleted by some users with special permissions, this is against the requirement.

Compliance:

- Object versions can't be overwritten or deleted by any user, including the root user

- Objects retention modes can't be changed, and retention periods can't be shortened

Governance:

- Most users can't overwrite or delete an object version or alter its lock settings

- Some users have special permissions to change the retention or delete the object

This solution meets the requirements of running a gaming application that transmits data by using UDP packets and scaling out and in as traffic increases and decreases. A Network Load Balancer can handle millions of requests per second while maintaining high throughput at ultra low latency, and it supports both TCP and UDP protocols. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and the scaling policies.

Option B is incorrect because an Application Load Balancer does not support UDP protocol, only HTTP and HTTPS. Option C is incorrect because Amazon Route 53 is a DNS service that can route traffic based on different policies, but it does not provide load balancing or scaling capabilities. Option D is incorrect because a NAT instance is used to enable instances in a private subnet to connect to the internet or other AWS services, but it does not provide load balancing or scaling capabilities.

References:

:

"With AWS Certificate Manager, you can quickly request a certificate, deploy it on ACM-integrated AWS resources, such as Elastic Load Balancers, Amazon CloudFront distributions, and APIs on API Gateway, and let AWS Certificate Manager handle certificate renewals. It also enables you to create private certificates for your internal resources and manage the certificate lifecycle centrally."

ce-ecs-taskdefinition.html

ElastiCache, enhances the performance of web applications by quickly retrieving information from fully-managed in-memory data stores. It utilizes Memcached and Redis, and manages to considerably reduce the time your applications would, otherwise, take to read data from disk-based databases. Amazon CloudFront supports dynamic content from HTTP and WebSocket protocols, which are based on the Transmission Control Protocol (TCP) protocol. Common use cases include dynamic API calls, web pages and web applications, as well as an application's static files such as audio and images. It also supports on-demand media streaming over HTTP. AWS Global Accelerator supports both User Datagram Protocol (UDP) and TCP-based protocols. It is commonly used for non-HTTP use cases, such as gaming, IoT and voice over IP. It is also good for HTTP use cases that need static IP addresses or fast regional failover

Service control policies (SCPs) are one type of policy that you can use to manage your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization, allowing you to ensure your accounts stay within your organization's access control guidelines. See https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scp.html.

Deploy the application servers by using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones. Use an Amazon RDS DB instance in a Multi-AZ configuration. To make an existing application highly available and resilient while avoiding any single points of failure and giving the application the ability to scale to meet user demand, the best solution would be to deploy the application servers using Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones and use an Amazon RDS DB instance in a Multi-AZ configuration. By using an Amazon RDS DB instance in a Multi-AZ configuration, the database is automatically replicated across multiple Availability Zones, ensuring that the database is highly available and can withstand the failure of a single Availability Zone. This provides fault tolerance and avoids any single points of failure.

High availability can be enabled for this architecture quite simply by modifying the existing Auto Scaling group to use multiple availability zones. The ASG will automatically balance the load so you don't actually need to specify the instances per AZ.

ples_vpc.html#example_vpc_2

https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

This solution meets the requirements of measuring the effectiveness of marketing campaigns by performing batch processing on csv files of sales data and storing the results in an Amazon S3 bucket once every hour. An AWS duo ETL process can use services such as AWS Glue or AWS Data Pipeline to extract data from S3, transform it into a more efficient format such as Apache Parquet, and load it back into S3. Apache Parquet is a columnar storage format that can improve the query performance and reliability of Athena by reducing the amount of data scanned, improving compression ratio, and enabling predicate pushdown.

This solution meets the requirements of a two-tier application that has a variable demand based on the time of day and must be available at all times, while minimizing the overall cost. EC2 Reserved Instances can provide significant savings compared to On-Demand Instances for the baseline level of usage, and they can guarantee capacity reservation when needed. EC2 Spot Instances can provide up to 90% savings compared to On-Demand Instances for any additional capacity that the application needs during peak hours. Spot Instances are suitable for stateless applications that can tolerate interruptions and can be replaced by other instances. Stopping the RDS database when it is not in use can reduce the cost of running the database tier.

Option A is incorrect because using all EC2 Spot Instances can affect the availability of the application if there are not enough spare capacity or if the Spot price exceeds the maximum price. Stopping the RDS database when it is not in use can reduce the cost of running the database tier, but it can also affect the availability of the application. Option B is incorrect because purchasing EC2 Instance Savings Plans to cover five EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Purchasing an RDS Reserved DB Instance can provide savings for the database tier, but it does not allow stopping the database when it is not in use. Option D is incorrect because purchasing EC2 Instance Savings Plans to cover two EC2 instances can lock in a fixed amount of compute usage per hour, which may not match the actual usage pattern of the application. Using up to three additional EC2 On-Demand Instances as needed can incur higher costs than using Spot Instances.

References:

A -> We can configure CloudFront to require HTTPS from clients (enhanced security) D - > storing static website on S3 provides scalability and less operational overhead, then configuration of Application LB and EC2 instances (hence E is out)

Reserved is cheaper than on demand the company has. And it's meet the availabilty (HA) requirement as to spot instance that can be disrupted at any time. PRICING BELOW. On-Demand: 0% There’s no commitment from you. You pay the most with this option. Reserved : 40%-60%1-year or 3-year commitment from you. You save money from that commitment. Spot 50%-90% Ridiculously inexpensive because there’s no commitment from the AWS side.

AWS Global Accelerator and Amazon CloudFront are separate services that use the AWS global network and its edge locations around the world. CloudFront improves performance for both cacheable content (such as images and videos) and dynamic content (such as API acceleration and dynamic site delivery). Global Accelerator improves performance for a wide range of applications over TCP or UDP by proxying packets at the edge to applications running in one or more AWS Regions. Global Accelerator is a good fit for non-HTTP use cases, such as gaming (UDP), IoT (MQTT), or Voice over IP, as well as for HTTP use cases that specifically require static IP addresses or deterministic, fast regional failover. Both services integrate with AWS Shield for DDoS protection.

with-amazon-sqs-and-amazon-sns/

M/latest/UserGuide/id_users.html

remiumsupport/knowledge-center/cloudwatch-memory-metrics-ec2/

We recommend that you use On-Demand Instances for applications with short-term, irregular workloads that cannot be interrupted. m/AWSEC2/latest/UserGuide/ec2-on-demand-instances.html

This solution meets the requirements of a customer-facing application that has a clearly defined access pattern throughout the year and a variable number of reads and writes that depend on the time of year. Amazon DynamoDB is a fully managed NoSQL database service that can handle any level of request traffic and data size. DynamoDB auto scaling can automatically adjust the provisioned read and write capacity based on the actual workload. DynamoDB on-demand backups can create full backups of the tables for data protection and archival purposes. DynamoDB Streams can capture a time-ordered sequence of item-level modifications in the tables for audit purposes.

Option B is incorrect because Amazon Redshift is a data warehouse service that is designed for analytical workloads, not for customer-facing applications. Option C is incorrect because Amazon RDS with Provisioned IOPS can provide consistent performance for relational databases, but it may not be able to handle unpredictable spikes in traffic and data size. Option D is incorrect because Amazon Aurora MySQL with auto scaling can provide high performance and availability for relational databases, but it does not support audit logging as a parameter.

References:

**AWS PrivateLink provides private connectivity between VPCs, AWS services, and your on-premises networks, without exposing your traffic to the public internet**. AWS PrivateLink makes it easy to connect services across different accounts and VPCs to significantly simplify your network architecture. Interface **VPC endpoints**, powered by AWS PrivateLink, connect you to services hosted by AWS Partners and supported solutions available in AWS Marketplace.

-transfer

The policy is separated into two parts because the ListBucket action requires permissions on the bucket while the other actions require permissions on the objects in the bucket. You must use two different Amazon Resource Names (ARNs) to specify bucket-level and object-level permissions. The first Resource element specifies arn:aws:s3:::AdminTools for the ListBucket action so that applications can list all objects in the AdminTools bucket.

The details are revealed in below url:

FIFO (First-In-First-Out) queues are designed to enhance messaging between applications when the order of operations and events is critical, or where duplicates can't be tolerated. Examples of situations where you might use FIFO queues include the following: To make sure that user-entered commands are run in the right order. To display the correct product price by sending price modifications in the right order. To prevent a student from enrolling in a course before registering for an account.

st/AuroraUserGuide/Aurora.Replication.html

#Aurora.Replication.Replicas Aurora Replicas have two main purposes. You can issue queries to them to scale the read operations for your application. You typically do so by connecting to the reader endpoint of the cluster. That way, Aurora can spread the load for read-only connections across as many Aurora Replicas as you have in the cluster. Aurora Replicas also help to increase availability. If the writer instance in a cluster becomes unavailable, Aurora automatically promotes one of the reader instances to take its place as the new writer.

RDS/latest/AuroraUserGuide/Aurora.Overview.html

ElastiCache can help speed up the read performance of the database by caching frequently accessed data, reducing latency and allowing the application to access the data more quickly. This solution requires minimal modifications to the current architecture, as ElastiCache can be used in conjunction with the existing Amazon RDS for MySQL database.

CloudFront uses a local cache to provide the response, AWS Global accelerator proxies requests and connects to the application all the time for the response.

t/latest/DeveloperGuide/private-content-restricting-access-to-s3.html#private-content-granting-permissions-to-oai

This solution meets the requirements of a serverless solution, encryption, replication, and SQL analysis with the least operational overhead. Amazon Athena is a serverless interactive query service that can analyze data in S3 using standard SQL. S3 Cross-Region Replication (CRR) can replicate encrypted objects to an S3 bucket in another Region automatically. Server-side encryption with AWS KMS multi-Region keys (SSE-KMS) can encrypt the data at rest using keys that are replicated across multiple Regions. Creating a new S3 bucket can avoid potential conflicts with existing data or configurations.

Option B is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. Option C is incorrect because server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not use KMS keys and it does not support multi-Region replication. Option D is incorrect because Amazon RDS is not a serverless solution and it cannot query data in S3 directly. It is also incorrect for the same reason as option C.

References:

This solution meets the requirements of saving money on storage while keeping the most accessed files readily available for the users. S3 Lifecycle policy can automatically move objects from one storage class to another based on predefined rules. S3 Standard-IA is a lower-cost storage class for data that is accessed less frequently, but requires rapid access when needed. It is suitable for ringtones older than 90 days that are downloaded infrequently.

Option A is incorrect because configuring S3 Standard-IA for the initial storage tier of the objects can incur higher costs for frequent access and retrieval fees. Option B is incorrect because moving the files to S3 Intelligent-Tiering can incur additional monitoring and automation fees that may not be necessary for ringtones older than 90 days. Option C is incorrect because using S3 inventory to manage objects and move them to S3 Standard-IA can be complex and time-consuming, and it does not provide automatic cost savings.

References:

( )

Amazon Athena is the best choice for running one-time queries on streaming data. Although Amazon Kinesis Data Analytics provides an easy and familiar standard SQL language to analyze streaming data in real-time, it is designed for continuous queries rather than one-time queries[1]. On the other hand, Amazon Athena is a serverless interactive query service that allows querying data in Amazon S3 using SQL. It is optimized for ad-hoc querying and is ideal for running one-time queries on streaming data[2].AWS Lake Formation uses as a central place to have all your data for analytics purposes (E). Athena integrate perfect with S3 and can makes queries (A).

SSE-S3 - is free and uses AWS owned CMKs (CMK = Customer Master Key). The encryption key is owned and managed by AWS, and is shared among many accounts. Its rotation is automatic with time that varies as shown in the table here. The time is not explicitly defined.

SSE-KMS - has two flavors:

AWS managed CMK. This is free CMK generated only for your account. You can only view it policies and audit usage, but not manage it. Rotation is automatic - once per 1095 days (3 years),

Customer managed CMK. This uses your own key that you create and can manage. Rotation is not enabled by default. But if you enable it, it will be automatically rotated every 1 year. This variant can also use an imported key material by you. If you create such key with an imported material, there is no automated rotation. Only manual rotation.

SSE-C - customer provided key. The encryption key is fully managed by you outside of AWS. AWS will not rotate it.

This solution meets the requirements of moving data to an Amazon S3 bucket, encrypting the data when it is stored in the S3 bucket, and automatically rotating the encryption key every year with the least operational overhead. AWS Key Management Service (AWS KMS) is a service that enables you to create and manage encryption keys for your data. A customer managed key is a symmetric encryption key that you create and manage in AWS KMS. You can enable automatic key rotation for a customer managed key, which means that AWS KMS generates new cryptographic material for the key every year. You can set the S3 bucket’s default encryption behavior to use the customer managed KMS key, which means that any object that is uploaded to the bucket without specifying an encryption method will be encrypted with that key.

Option A is incorrect because using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) does not allow you to control or manage the encryption keys. SSE-S3 uses a unique key for each object, and encrypts that key with a master key that is regularly rotated by S3. However, you cannot enable or disable key rotation for SSE-S3 keys, or specify the rotation interval. Option C is incorrect because manually rotating the KMS key every year can increase the operational overhead and complexity, and it may not meet the requirement of rotating the key every year if you forget or delay the rotation process. Option D is incorrect because encrypting the data with customer key material before moving the data to the S3 bucket can increase the operational overhead and complexity, and it may not provide consistent encryption for all objects in the bucket. Creating a KMS key without key material and importing the customer key material into the KMS key can enable you to use your own source of random bits to generate your KMS keys, but it does not support automatic key rotation.

References:

For standard accelerators, Global Accelerator uses the AWS global network to route traffic to the optimal regional endpoint based on health, client location, and policies that you configure, which increases the availability of your applications. Endpoints for standard accelerators can be Network Load Balancers, Application Load Balancers, Amazon EC2 instances, or Elastic IP addresses that are located in one AWS Region or multiple Regions.

Q: What does Amazon RDS manage on my behalf?

Amazon RDS manages the work involved in setting up a relational database: from provisioning the infrastructure capacity you request to installing the database software. Once your database is up and running, Amazon RDS automates common administrative tasks such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

1. lowest possible latency + node to node ==> cluster placement(must be within one AZ), so C, D out

2. For EBS Multi-Attach, up to 16 instances can be attached to a single volume==>we have 16 linux instance==>more close to A

3. "need a shared block device volume"==>EBS Multi-attach is Block Storage whereas EFS is File Storage==> B out

4. EFS automatically replicates data within and across 3 AZ==>we use cluster placement so all EC2 are within one AZ.

5. EBS Multi-attach volumes can be used for clients within a single AZ.

In a typical development environment, dev and test databases are mostly utilized for 8 hours a day and sit idle when not in use. However, the databases are billed for the compute and storage costs during this idle time. To reduce the overall cost, Amazon RDS allows instances to be stopped temporarily. While the instance is stopped, you’re charged for storage and backups, but not for the DB instance hours. Please note that a stopped instance will automatically be started after 7 days. This post presents a solution using AWS Lambda and Amazon EventBridge that allows you to schedule a Lambda function to stop and start the idle databases with specific tags to save on compute costs. The second post presents a solution that accomplishes stop and start of the idle Amazon RDS databases using AWS Systems Manager.

Migrate the database to Amazon Aurora MySQL - this will let the DB scale on it's own; it'll scale automatically without needing adjustment. Create AMI of the web app and using a launch template - this will make the creating of any future instances of the app seamless. They can then be added to the auto scaling group which will save them money as it will scale up and down based on demand. Using a spot fleet to launch instances- This solves the "MOST cost-effective" portion of the question as spot instances come at a huge discount at the cost of being terminated at any time Amazon deems fit. I think this is why there's a bit of disagreement on this. While it's the most cost effective, it would be a terrible choice if amazon were to terminate that spot instance during a busy period.

Amazon Aurora is a fully managed, scalable, and highly available relational database service that is compatible with PostgreSQL. Migrating the database to Amazon Aurora would reduce the operational overhead of maintaining the database infrastructure and allow the company to focus on building and scaling the application. AWS Fargate is a fully managed container orchestration service that enables users to run containers without the need to manage the underlying EC2 instances. By using AWS Fargate with Amazon Elastic Container Service (Amazon ECS), the solutions architect can improve the scalability and efficiency of the web application and reduce the operational overhead of maintaining the underlying infrastructure.

This is the purpose of bookmarks: "AWS Glue tracks data that has already been processed during a previous run of an ETL job by persisting state information from the job run. This persisted state information is called a job bookmark. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data."

These are some of the main use cases for AWS DataSync: • Data migration – Move active datasets rapidly over the network into Amazon S3, Amazon EFS, or FSx for Windows File Server. DataSync includes automatic encryption and data integrity validation to help make sure that your data arrives securely, intact, and ready to use.

"DataSync includes encryption and integrity validation to help make sure your data arrives securely, intact, and ready to use." https://aws.amazon.com/datasync/faqs/

To meet the requirements of immediately accessible records for 1 year and then archived for an additional 9 years with maximum resiliency, we can use S3 Lifecycle policy to transition records from S3 Standard to S3 Glacier Deep Archive after 1 year. And to ensure that the records cannot be deleted by anyone, including administrative and root users, we can use S3 Object Lock in compliance mode for a period of 10 years. Therefore, the correct answer is option C.

Reserve instances: You will have to pay for the whole term (1 year or 3years) which is not cost effective

AWS Cost Explorer is a tool that enables you to view and analyze your costs and usage. You can explore your usage and costs using the main graph, the Cost Explorer cost and usage reports, or the Cost Explorer RI reports. You can view data for up to the last 12 months, forecast how much you're likely to spend for the next 12 months, and get recommendations for what Reserved Instances to purchase. You can use Cost Explorer to identify areas that need further inquiry and see trends that you can use to understand your costs. management/latest/userguide/ce-what-is.html

"In some cases, this connection alone is not enough. It is always better to guarantee a fallback connection as the backup of DX. There are several options, but implementing it with an AWS Site-To-Site VPN is a real cost-effective solution that can be exploited to reduce costs or, in the meantime, wait for the setup of a second DX."

Security groups are stateful. All inbound traffic is blocked by default. If you create an inbound rule allowing traffic in, that traffic is automatically allowed back out again. You cannot block specific IP address using Security groups (instead use Network Access Control Lists).

"You can specify allow rules, but not deny rules." "When you first create a security group, it has no inbound rules. Therefore, no inbound traffic originating from another host to your instance is allowed until you add inbound rules to the security group." Source: oups

organization-of-iam-principals/

The aws:PrincipalOrgID global key provides an alternative to listing all the account IDs for all AWS accounts in an organization. For example, the following Amazon S3 bucket policy allows members of any account in the XXX organization to add an object into the examtopics bucket.

{"Version": "2020-09-10",

"Statement": {

"Sid": "AllowPutObject",

"Effect": "Allow",

"Principal": "*",

"Action": "s3:PutObject",

"Resource": "arn:aws:s3:::examtopics/*",

"Condition": {"StringEquals":

{"aws:PrincipalOrgID":["XXX"]}}}}

rtificate-manager/faqs/#Managed_renewal_and_deployment

&sec=ft

To launch a one-deal-a-day website on AWS with millisecond latency during peak hours and with the least operational overhead, the best option is to use an Amazon S3 bucket to host the website's static content, deploy an Amazon CloudFront distribution, set the S3 bucket as the origin, use Amazon API Gateway and AWS Lambda functions for the backend APIs, and store the data in Amazon DynamoDB. This option requires minimal operational overhead and can handle millions of requests each hour with millisecond latency during peak hours. Therefore, option D is the correct answer.

To provide the product manager access to the Amazon CloudWatch dashboard while following the principle of least privilege, a solution architect should create an IAM user specifically for the product manager and attach the CloudWatch Read Only Access managed policy to the user. This policy allows the user to view the dashboard without being able to make any changes to it. The solution architect should then share the new login credential with the product manager and provide them with the browser URL of the correct dashboard.

The basic difference between Snowball and Snowball Edge is the capacity they provide. Snowball provides a total of 50 TB or 80 TB, out of which 42 TB or 72 TB is available, while Amazon Snowball Edge provides 100 TB, out of which 83 TB is available.

ction-using-aws-gateway-load-balancer/

"Security groups create an outbound rule for every inbound rule." Not completely right. Statefull does NOT mean that if you create an inbound (or outbound) rule, it will create an outbound (or inbound) rule. What it does mean is: suppose you create an inbound rule on port 443 for the X ip. When a request enters on port 443 from X ip, it will allow traffic out for that request in the port 443. However, if you look at the outbound rules, there will not be any outbound rule on port 443 unless explicitly create it. In ACLs, which are stateless, you would have to create an inbound rule to allow incoming requests and an outbound rule to allow your application responds to those incoming requests.

https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#SecurityGroupRules

You might want to use Transfer Acceleration on a bucket for various reasons, including the following:

You have customers that upload to a centralized bucket from all over the world.

You transfer gigabytes to terabytes of data on a regular basis across continents.

You are unable to utilize all of your available bandwidth over the Internet when uploading to Amazon S3.

acceleration/#:~:text=S3%20Transfer%20Acceleration%20(S3TA)%20reduces,to%20S3%20for%20remote%20applications:

"Amazon S3 Transfer Acceleration can speed up content transfers to and from Amazon S3 by as much as 50-500% for long-distance transfer of larger objects. Customers who have either web or mobile applications with widespread users or applications hosted far away from their S3 bucket can experience long and variable upload and download speeds over the Internet"

"Improved throughput - You can upload parts in parallel to improve throughput."

firehose/features/?nc=sn&loc=2#:~:text=into%20Amazon%20S3%2C%20Amazon%20Redshift%2C%20Amazon%20OpenSearch%20Service%2C%20Kinesis,Delivery%20streams

VPC endpoint allows you to connect to AWS services using a private network instead of using the public Internet

Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations around the world, providing low latency and high transfer speeds to users accessing the content. Adding a CloudFront distribution in front of the S3 bucket will cache the static website's content at edge locations around the world, decreasing latency for users accessing the website. This solution is also cost-effective as it only charges for the data transfer and requests made by users accessing the content from the CloudFront edge locations. Additionally, this solution provides scalability and reliability benefits as CloudFront can automatically scale to handle increased demand and provide high availability for the website.

The destination of your Kinesis Data Firehose delivery stream. Kinesis Data Firehose can send data records to various destinations, including Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service, and any HTTP endpoint that is owned by you or any of your third-party service providers. The following are the supported destinations:

* Amazon OpenSearch Service

* Amazon S3

* Datadog

* Dynatrace

* Honeycomb

* HTTP Endpoint

* Logic Monitor

* MongoDB Cloud

* New Relic

* Splunk

* Sumo Logic

Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events.

Amazon Athena can be used to query JSON in S3

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"Typically, you configure buckets to be Requester Pays buckets when you want to share data but not incur charges associated with others accessing the data. For example, you might use Requester Pays buckets when making available large datasets, such as zip code directories, reference data, geospatial information, or web crawling data." azonS3/latest/userguide/RequesterPaysBuckets.html

To meet the requirements of detecting and alerting the administrators when PII is shared and automating remediation with the least development effort, the best approach would be to use Amazon S3 bucket as a secure transfer point and scan the objects in the bucket with Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data stored in Amazon S3. It can be used to classify sensitive data, monitor access to sensitive data, and automate remediation actions.

In this scenario, after uploading the files to the Amazon S3 bucket, the objects can be scanned for PII by Amazon Macie, and if it detects any PII, it can trigger an Amazon Simple Notification Service (SNS) notification to alert the administrators to remove the objects containing PII. This approach requires the least development effort, as Amazon Macie already has pre-built data classification rules that can detect PII in various formats.

Hence, option B is the correct answer.

References:

Amazon Macie User Guide:

AWS Well-Architected Framework - Security Pillar:

"For archive data that needs immediate access, such as medical images, news media assets, or genomics data, choose the S3 Glacier Instant Retrieval storage class, an archive storage class that delivers the lowest cost storage with milliseconds retrieval. For archive data that does not require immediate access but needs the flexibility to retrieve large sets of data at no cost, such as backup or disaster recovery use cases, choose S3 Glacier Flexible Retrieval (formerly S3 Glacier), with retrieval in minutes or free bulk retrievals in 5-12 hours."

To clone the production data into the test environment with high I/O performance and without affecting the production environment, the best option is to take EBS snapshots of the production EBS volumes and restore them onto new EBS volumes in the test environment. Then, attach the new EBS volumes to EC2 instances in the test environment. This option minimizes the time required to clone the data and ensures that modifications to the cloned data do not affect the production environment. Therefore, option C is the correct answer.

objects

This solution meets the requirements of scalability, performance, and availability. AWS Lambda can process the photos in parallel and scale up or down automatically depending on the demand. Amazon S3 can store the photos and metadata reliably and durably, and provide high availability and low latency. DynamoDB can store the metadata efficiently and provide consistent performance. This solution also reduces the cost and complexity of managing EC2 instances and EBS volumes.

Option A is incorrect because storing the photos in DynamoDB is not a good practice, as it can increase the storage cost and limit the throughput. Option B is incorrect because Kinesis Data Firehose is not designed for processing photos, but for streaming data to destinations such as S3 or Redshift. Option D is incorrect because increasing the number of EC2 instances and using Provisioned IOPS SSD volumes does not guarantee scalability, as it depends on the load balancer and the application code. It also increases the cost and complexity of managing the infrastructure.

References:

The storage solution that will meet these requirements most cost-effectively is B: Create an S3 Lifecycle configuration to transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. Amazon S3 Glacier Deep Archive is a secure, durable, and extremely low-cost Amazon S3 storage class for long-term retention of data that is rarely accessed and for which retrieval times of several hours are acceptable. It is the lowest-cost storage option in Amazon S3, making it a cost-effective choice for storing backup files that are not accessed after 1 month. You can use an S3 Lifecycle configuration to automatically transition objects from S3 Standard to S3 Glacier Deep Archive after 1 month. This will minimize the storage costs for the backup files that are not accessed frequently.

Amazon QuickSight is a data visualization service that allows you to create interactive dashboards and reports from various data sources, including Amazon S3 and Amazon RDS for PostgreSQL. You can connect all the data sources and create new datasets in QuickSight, and then publish dashboards to visualize the data. You can also share the dashboards with the appropriate users and groups, and control their access levels using IAM roles and permissions.

S3 Intelligent-Tiering - Perfect use case when you don't know the frequency of access or irregular patterns of usage.

Amazon S3 offers a range of storage classes designed for different use cases. These include S3 Standard for general-purpose storage of frequently accessed data; S3 Intelligent-Tiering for data with unknown or changing access patterns; S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) for long-lived, but less frequently accessed data; and Amazon S3 Glacier (S3 Glacier) and Amazon S3 Glacier Deep Archive (S3 Glacier Deep Archive) for long-term archive and digital preservation. If you have data residency requirements that can’t be met by an existing AWS Region, you can use the S3 Outposts storage class to store your S3 data on-premises. Amazon S3 also offers capabilities to manage your data throughout its lifecycle. Once an S3 Lifecycle policy is set, your data will automatically transfer to a different storage class without any changes to your application.

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anager-multiple-regions/

To design the API Gateway URL with the company's domain name and corresponding certificate, the company needs to do the following: 1. Create a Regional API Gateway endpoint: This will allow the company to create an endpoint that is specific to a region. 2. Associate the API Gateway endpoint with the company's domain name: This will allow the company to use its own domain name for the API Gateway URL. 3. Import the public certificate associated with the company's domain name into AWS Certificate Manager (ACM) in the same Region: This will allow the company to use HTTPS for secure communication with its APIs. 4. Attach the certificate to the API Gateway endpoint: This will allow the company to use the certificate for securing the API Gateway URL. 5. Configure Route 53 to route traffic to the API Gateway endpoint: This will allow the company to use Route 53 to route traffic to the API Gateway URL using the company's domain name.

EFS is a standard file system, it scales automatically and is highly available.

Amazon S3 File Gateway is a hybrid cloud storage service that enables on-premises applications to seamlessly use Amazon S3 cloud storage. It provides a file interface to Amazon S3 and supports SMB and NFS protocols. It also supports S3 Lifecycle policies that can automatically transition data from S3 Standard to S3 Glacier Deep Archive after a specified period of time. This solution will meet the requirements of increasing the company’s available storage space without losing low-latency access to the most recently accessed files and providing file lifecycle management to avoid future storage issues.

AWS Config is a fully managed service that allows the company to assess, audit, and evaluate the configurations of its AWS resources. It provides a detailed inventory of the resources in use and tracks changes to resource configurations. AWS Config can detect configuration changes and alert the company when changes occur. It also provides a historical view of changes, which is essential for compliance and governance purposes. AWS CloudTrail is a fully managed service that provides a detailed history of API calls made to the company's AWS resources. It records all API activity in the AWS account, including who made the API call, when the call was made, and what resources were affected by the call. This information is critical for security and auditing purposes, as it allows the company to investigate any suspicious activity that might occur on its AWS resources.

This option is the most operationally efficient way to meet the requirements because it allows the company to monitor and analyze the database login activity across all the accounts in the organization. By publishing the Aurora general logs to a log group in Amazon CloudWatch Logs, the company can enable the logging of the database connections, disconnections, and failed authentication attempts. By exporting the log data to a central Amazon S3 bucket, the company can store the log data in a durable and cost-effective way and use other AWS services or tools to perform further analysis or alerting on the log data. For example, the company can use Amazon Athena to query the log data in Amazon S3, or use Amazon SNS to send notifications based on the log data.

A. Attach service control policies (SCPs) to the root of the organization to identify the failed login attempts. This option is not effective because SCPs are not designed to identify the failed login attempts, but to restrict the actions that the users and roles can perform in the member accounts of the organization. SCPs are applied to the AWS API calls, not to the database login attempts. Moreover, SCPs do not provide any logging or analysis capabilities for the database activity.

B. Enable the Amazon RDS Protection feature in Amazon GuardDuty for the member accounts of the organization. This option is not optimal because the Amazon RDS Protection feature in Amazon GuardDuty is not available for Aurora PostgreSQL databases, but only for Amazon RDS for MySQL and Amazon RDS for MariaDB databases. Moreover, the Amazon RDS Protection feature does not monitor the database login attempts, but the network and API activity related to the RDS instances.

D. Publish all the Aurora PostgreSQL database events in AWS CloudTrail to a central Amazon S3 bucket. This option is not sufficient because AWS CloudTrail does not capture the database login attempts, but only the AWS API calls made by or on behalf of the Aurora PostgreSQL database. For example, AWS CloudTrail can record the events such as creating, modifying, or deleting the database instances, clusters, or snapshots, but not the events such as connecting, disconnecting, or failing to authenticate to the database.

References:

1 Working with Amazon Aurora PostgreSQL - Amazon Aurora

2 Working with log groups and log streams - Amazon CloudWatch Logs

3 Exporting Log Data to Amazon S3 - Amazon CloudWatch Logs

[4] Amazon GuardDuty FAQs

[5] Logging Amazon RDS API Calls with AWS CloudTrail - Amazon Relational Database Service

Amazon Aurora Serverless v2 is a cost-effective solution that can automatically scale the database capacity up and down based on the application’s needs. It can handle unpredictable traffic spikes without requiring any provisioning or management of database instances. It is compatible with PostgreSQL and offers high performance, availability, and durability1. References: 1: AWS Ramp-Up Guide: Architect2, page 312: AWS Certified Solutions Architect - Associate exam guide3, page 9.

The correct solution is to create an Object Lambda Access Point and an AWS Lambda function that redacts the PII when the function reads the file. This way, the company can use the S3 Object Lambda feature to modify the S3 object content on the fly, without creating a copy or changing the original object. The external service provider can access the Object Lambda Access Point and get the redacted version of the file. This solution has the least operational overhead because it does not require any additional storage, processing, or synchronization. The solution also scales automatically with the number of customer conversations and the demand from the external service provider. The other options are incorrect because:

Option B is using a batch process on an EC2 instance to read, redact, and write the files to a different S3 bucket. This solution has more operational overhead because it requires managing the EC2 instance, the batch process, and the additional S3 bucket. It also introduces latency and inconsistency between the original and the redacted files.

Option C is using a web application on an EC2 instance to present, redact, and download the files. This solution has more operational overhead because it requires managing the EC2 instance, the web application, and the download process. It also exposes the original files to the web application, which increases the risk of leaking the PII.

Option D is using a DynamoDB table and a Lambda function to store the non-PII data from the files. This solution has more operational overhead because it requires managing the DynamoDB table, the Lambda function, and the data transformation. It also changes the format and the structure of the original files, which may affect the quality control process.

References:

S3 Object Lambda

Object Lambda Access Point

Lambda function

The most cost-effective solution for the online video game company is to configure a Network Load Balancer with the required protocol and ports for the internet traffic and specify the EC2 instances as the targets. This solution will enable the company to handle millions of UDP requests per second with ultra-low latency and high performance.

A Network Load Balancer is a type of Elastic Load Balancing that operates at the connection level (Layer 4) and routes traffic to targets (EC2 instances, microservices, or containers) within Amazon VPC based on IP protocol data. A Network Load Balancer is ideal for load balancing of both TCP and UDP traffic, as it is capable of handling millions of requests per second while maintaining high throughput at ultra-low latency. A Network Load Balancer also preserves the source IP address of the clients to the back-end applications, which can be useful for logging or security purposes1.

The solution that will meet the requirements is to create Amazon Kinesis data streams for data ingestion, create Amazon Kinesis Data Firehose delivery streams to consume the Kinesis data streams, and specify the S3 bucket as the destination of the delivery streams. This solution will allow the company’s application to ingest real-time data from third-party applications and place the ingested raw data in an S3 bucket. Amazon Kinesis data streams are scalable and durable streams that can capture and store data from hundreds of thousands of sources. Amazon Kinesis Data Firehose is a fully managed service that can deliver streaming data to destinations such as S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Amazon Kinesis Data Firehose can also transform and compress the data before delivering it to S3.

The other solutions are not as effective as the first one because they either do not support real-time data ingestion, do not work with third-party applications, or do not use S3 as the destination. Creating database migration tasks in AWS Database Migration Service (AWS DMS) will not support real-time data ingestion, as AWS DMS is mainly designed for migrating relational databases, not streaming data. AWS DMS also requires replication instances, source endpoints, and target endpoints to be compatible with specific database engines and versions. Creating and configuring AWS DataSync agents on the EC2 instances will not work with third-party applications, as AWS DataSync is a service that transfers data between on-premises storage systems and AWS storage services, not between applications. AWS DataSync also requires installing agents on the source or destination servers. Creating an AWS Direct Connect connection to the application for data ingestion will not use S3 as the destination, as AWS Direct Connect is a service that establishes a dedicated network connection between on-premises and AWS, not between applications and storage services. AWS Direct Connect also requires a physical connection to an AWS Direct Connect location.

References:

Amazon Kinesis

Amazon Kinesis Data Firehose

AWS Database Migration Service

AWS DataSync

AWS Direct Connect

A Network Load Balancer can be assigned one Elastic IP address for each Availability Zone it uses1. This allows the clients to reach the load balancer using a static IP address that can be authorized on their firewalls. An Application Load Balancer cannot be assigned an Elastic IP address2. An A record in an Amazon Route 53 hosted zone pointing to an Elastic IP address would not work because the load balancer would still use its own IP address as the source of the forwarded requests to the web service. An EC2 instance with a public IP address running as a proxy in front of the load balancer would add unnecessary complexity and cost, and would not provide the same scalability and availability as a Network Load Balancer. References: 1: Network Load Balancers - Elastic Load Balancing3, IP address type section2: How to assign Elastic IP to Application Load Balancer in AWS?4, answer section.

This option is the most cost-effective solution because it leverages the Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Spot Instances can be interrupted by AWS when the demand for On-Demand instances increases, but since the batch jobs are fault-tolerant and can be reprocessed by another instance, this is not a major issue. By using a launch template, the company can specify the configuration of the Spot Instances, such as the instance type, the operating system, and the user data. By using an Auto Scaling group, the company can automatically scale the number of Spot Instances based on the CPU usage, which reflects the load of the batch jobs. This way, the company can optimize the performance and the cost of the EC2 instances for the nightly batch jobs.

A. Purchase a 1-year Savings Plan for Amazon EC2 that covers the instance family of the Auto Scaling group that the batch job uses. This option is not optimal because it requires a commitment to a consistent amount of compute usage per hour for a one-year term, regardless of the instance type, size, region, or operating system. This can limit the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Savings Plans do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Savings Plans.

B. Purchase a 1-year Reserved Instance for the specific instance type and operating system of the instances in the Auto Scaling group that the batch job uses. This option is not ideal because it requires a commitment to a specific instance configuration for a one-year term, which can reduce the flexibility and scalability of the Auto Scaling group and result in overpaying for unused compute capacity. Moreover, Reserved Instances do not provide a capacity reservation, which means the company still needs to reserve capacity with On-Demand Capacity Reservations and pay lower prices with Reserved Instances.

D. Create a new launch template for the Auto Scaling group Increase the instance size Set a policy to scale out based on CPU usage. This option is not cost-effective because it does not take advantage of the lower prices of Spot Instances. Increasing the instance size can improve the performance of the batch jobs, but it can also increase the cost of the On-Demand instances. Moreover, scaling out based on CPU usage can result in launching more instances than needed, which can also increase the cost of the system.

References:

1 Spot Instances - Amazon Elastic Compute Cloud

2 Launch templates - Amazon Elastic Compute Cloud

3 Auto Scaling groups - Amazon EC2 Auto Scaling

[4] Savings Plans - Amazon EC2 Reserved Instances and Other AWS Reservation Models

The solution that will meet the requirements is to deploy AWS Storage Gateway using cached volumes and use Storage Gateway to store data in Amazon S3 while retaining copies of frequently accessed data subsets locally. This solution will allow the company to migrate its storage infrastructure to AWS while minimizing bandwidth costs, as it will only transfer data that is not cached locally. The solution will also allow for immediate retrieval of data at no additional cost, as the cached volumes will provide low-latency access to the most recently used data. The data stored in Amazon S3 will be durable, scalable, and secure.

The other solutions are not as effective as the first one because they either do not meet the requirements or introduce additional costs or complexity. Deploying Amazon S3 Glacier Vault and enabling expedited retrieval will not meet the requirements, as it will incur additional costs for both storage and retrieval. Amazon S3 Glacier is a low-cost storage service for data archiving and backup, but it has longer retrieval times than Amazon S3. Expedited retrieval is a feature that allows faster access to data, but it charges a higher fee per GB retrieved. Provisioned retrieval capacity is a feature that reserves dedicated capacity for expedited retrievals, but it also charges a monthly fee per provisioned capacity unit. Deploying AWS Storage Gateway using stored volumes to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will not migrate the storage infrastructure to AWS, but only create backups. Stored volumes are volumes that store the primary data locally and back up snapshots to Amazon S3. This solution will not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage. Deploying AWS Direct Connect to connect with the on-premises data center and configuring AWS Storage Gateway to store data locally and use Storage Gateway to asynchronously back up point-in-time snapshots of the data to Amazon S3 will not meet the requirements, as it will also not migrate the storage infrastructure to AWS, but only create backups. AWS Direct Connect is a service that establishes a dedicated network connection between the on-premises data center and AWS, which can reduce network costs and increase bandwidth. However, this solution will also not reduce the storage capacity needed on-premises, nor will it leverage the benefits of cloud storage.

References:

AWS Storage Gateway

Cached volumes - AWS Storage Gateway

Amazon S3 Glacier

Retrieving archives from Amazon S3 Glacier vaults - Amazon Simple Storage Service

Stored volumes - AWS Storage Gateway

AWS Direct Connect

This solution meets the following requirements:

It is private and secure, as it allows the EC2 instances to access the S3 bucket without using the public internet. A VPC endpoint is a gateway that enables you to create a private connection between your VPC and another AWS service, such as S3, within the same Region. A VPC endpoint for S3 provides secure and direct access to S3 buckets and objects using private IP addresses from your VPC. You can also use VPC endpoint policies and S3 bucket policies to control the access to the S3 resources based on the endpoint, the IAM user, the IAM role, or the source IP address.

It is simple and scalable, as it does not require any additional AWS services, gateways, or NAT devices. A VPC endpoint for S3 is a fully managed service that scales automatically with the network traffic. You can create a VPC endpoint for S3 with a few clicks in the VPC console or with a simple API call. You can also use the same VPC endpoint to access multiple S3 buckets in the same Region.

References:

VPC Endpoints - Amazon Virtual Private Cloud

Gateway VPC endpoints - Amazon Virtual Private Cloud

Using Amazon S3 with interface VPC endpoints - Amazon Simple Storage Service

Using Amazon S3 with gateway VPC endpoints - Amazon Simple Storage Service

The company wants to reduce the compute costs and maintain service latency for its Lambda functions that process a constantly increasing number of messages in a message queue. The Lambda functions use CPU intensive code to process the messages. To meet these requirements, a solutions architect should recommend the following solution:

Configure provisioned concurrency for the Lambda functions. Provisioned concurrency is the number of pre-initialized execution environments that are allocated to the Lambda functions. These execution environments are prepared to respond immediately to incoming function requests, reducing the cold start latency. Configuring provisioned concurrency also helps to avoid throttling errors due to reaching the concurrency limit of the Lambda service.

Increase the memory according to AWS Compute Optimizer recommendations. AWS Compute Optimizer is a service that provides recommendations for optimal AWS resource configurations based on your utilization data. By increasing the memory allocated to the Lambda functions, you can also increase the CPU power and improve the performance of your CPU intensive code. AWS Compute Optimizer can help you find the optimal memory size for your Lambda functions based on your workload characteristics and performance goals.

This solution will reduce the compute costs by avoiding unnecessary over-provisioning of memory and CPU resources, and maintain service latency by using provisioned concurrency and optimal memory size for the Lambda functions.

References:

Provisioned Concurrency

AWS Compute Optimizer

AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. You can create Lambda functions using various languages, including Java, and specify the amount of memory and CPU allocated to your function. Lambda charges you only for the compute time you consume, which is calculated based on the number of requests and the duration of your code execution. You can use Amazon EventBridge to trigger your Lambda function on a schedule, such as every hour, using cron or rate expressions. This solution will optimize the costs to run the job, as you will not pay for any idle time or unused resources, unlike running the job on an EC2 instance. References: 1: AWS Lambda - FAQs2, General Information section2: Tutorial: Schedule AWS Lambda functions using EventBridge3, Introduction section3: Schedule expressions using rate or cron - AWS Lambda4, Introduction section.

The most suitable solution for optimizing the user experience by providing the lowest latency for content uploads is to upload and store content in Amazon S3 and use S3 Transfer Acceleration for the uploads. This solution will enable the company to leverage the AWS global network and edge locations to speed up the data transfer between the users and the S3 buckets.

Amazon S3 is a storage service that provides scalable, durable, and highly available object storage for any type of data. Amazon S3 allows users to store and retrieve data from anywhere on the web, and offers various features such as encryption, versioning, lifecycle management, and replication1.

S3 Transfer Acceleration is a feature of Amazon S3 that helps users transfer data to and from S3 buckets more quickly. S3 Transfer Acceleration works by using optimized network paths and Amazon’s backbone network to accelerate data transfer speeds. Users can enable S3 Transfer Acceleration for their buckets and use a distinct URL to access them, such as .s3-accelerate.amazonaws.com2.

The other options are not correct because they either do not provide the lowest latency or are not suitable for the use case. Uploading and storing content in Amazon S3 and using Amazon CloudFront for the uploads is not correct because this solution is not designed for optimizing uploads, but rather for optimizing downloads. Amazon CloudFront is a content delivery network (CDN) that helps users distribute their content globally with low latency and high transfer speeds. CloudFront works by caching the content at edge locations around the world, so that users can access it quickly and easily from anywhere3. Uploading content to Amazon EC2 instances in the Region that is closest to the user and copying the data to Amazon S3 is not correct because this solution adds unnecessary complexity and cost to the process. Amazon EC2 is a computing service that provides scalable and secure virtual servers in the cloud. Users can launch, stop, or terminate EC2 instances as needed, and choose from various instance types, operating systems, and configurations4. Uploading and storing content in Amazon S3 in the Region that is closest to the user and using multiple distributions of Amazon CloudFront is not correct because this solution is not cost-effective or efficient for the use case. As mentioned above, Amazon CloudFront is a CDN that helps users distribute their content globally with low latency and high transfer speeds. However, creating multiple CloudFront distributions for each Region would incur additional charges and management overhead, and would not be necessary since 90% of the content is consumed within the same Region where it is uploaded3.

References:

What Is Amazon Simple Storage Service? - Amazon Simple Storage Service

Amazon S3 Transfer Acceleration - Amazon Simple Storage Service

What Is Amazon CloudFront? - Amazon CloudFront

What Is Amazon EC2? - Amazon Elastic Compute Cloud

This solution meets the following requirements:

It is operationally efficient, as it only requires one activation of the cost allocation tag and one creation of the cost report from the management account, which has access to all the member accounts’ data and billing preferences.

It is consistent, as it uses the AWS-defined cost allocation tag named department, which is automatically applied to resources when the company creates tags using the tagging policy enforced by AWS Organizations. This ensures that the tag name and value are the same across all the resources and accounts, and avoids any discrepancies or errors that might arise from user-defined tags.

It is informative, as it creates one cost report in Cost Explorer grouping by the tag name, and filters by EC2. This allows the accounting team to see the breakdown of EC2 consumption and costs by department, regardless of the AWS account. The team can also use other features of Cost Explorer, such as charts, filters, and forecasts, to analyze and optimize the spending.

References:

Using AWS cost allocation tags - AWS Billing

User-defined cost allocation tags - AWS Billing

Cost Tagging and Reporting with AWS Organizations

Point-in-time recovery (PITR) for DynamoDB is a feature that enables you to restore your table data to any point in time during the last 35 days. PITR helps protect your table from accidental write or delete operations, such as a test script writing to a production table or a user issuing a wrong command. PITR is easy to use, fully managed, fast, and scalable. You can enable PITR with a single click in the DynamoDB console or with a simple API call. You can restore a table to a new table using the console, the AWS CLI, or the DynamoDB API. PITR does not consume any provisioned table capacity and has no impact on the performance or availability of your production applications. PITR meets the requirements of the company with the least operational overhead, as it does not require any manual backup creation, scheduling, or maintenance. It also provides per-second granularity for restoring the table to any point within the last 24 hours.

References:

Point-in-time recovery for DynamoDB - Amazon DynamoDB

Amazon DynamoDB point-in-time recovery (PITR)

Enable Point-in-Time Recovery (PITR) for Dynamodb global tables

Restoring a DynamoDB table to a point in time - Amazon DynamoDB

Point-in-time recovery: How it works - Amazon DynamoDB

This solution provides the appropriate user access most cost-effectively because it uses the Amazon S3 Intelligent-Tiering storage class, which automatically optimizes storage costs by moving data to the most cost-effective access tier when access patterns change, without performance impact or operational overhead1. This storage class is ideal for data with unknown, changing, or unpredictable access patterns, such as photos that are heavily viewed for months or less than a week. By storing the photo metadata and its S3 location in DynamoDB, the application can quickly query and retrieve the relevant photos for each user. DynamoDB is a fast, scalable, and fully managed NoSQL database service that supports key-value and document data models2.

References: 1: Amazon S3 Intelligent-Tiering Storage Class | AWS3, Overview section2: Amazon DynamoDB - NoSQL Cloud Database Service4, Overview section.

AWS WAF is a web application firewall that helps protect web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources. AWS WAF allows users to create rules that block, allow, or count web requests based on customizable web security rules. One of the types of rules that can be created is an IP match rule, which allows users to specify a list of IP addresses or IP address ranges that they want to allow or block. By modifying the configuration of AWS WAF to add an IP match condition to block the malicious IP address, the solution architect can prevent the attacker from accessing the website through the CloudFront distribution and the ALB.

The other options are not correct because they do not effectively block the malicious IP address from accessing the website. Modifying the network ACL on the CloudFront distribution or the EC2 instances in the target groups behind the ALB will not work because network ACLs are stateless and do not evaluate traffic at the application layer. Modifying the security groups for the EC2 instances in the target groups behind the ALB will not work because security groups are stateful and only evaluate traffic at the instance level, not at the load balancer level.

References:

AWS WAF

How AWS WAF works

Working with IP match conditions

This option is the most secure and simple way to encrypt the secrets that are stored in Amazon EKS. AWS Key Management Service (AWS KMS) is a service that allows you to create and manage encryption keys that can be used to encrypt your data. Amazon EKS KMS secrets encryption is a feature that enables you to use a KMS key to encrypt the secrets that are stored in the Kubernetes etcd key-value store. This provides an additional layer of protection for your sensitive data, such as passwords, tokens, and keys. You can create a new KMS key or use an existing one, and then enable the Amazon EKS KMS secrets encryption on the Amazon EKS cluster. You can also use IAM policies to control who can access or use the KMS key.

Option A is not correct because using AWS Secrets Manager to manage, rotate, and store all secrets in Amazon EKS is not necessary or efficient. AWS Secrets Manager is a service that helps you securely store, retrieve, and rotate your secrets, such as database credentials, API keys, and passwords. You can use it to manage secrets that are used by your applications or services outside of Amazon EKS, but it is not designed to encrypt the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using AWS Secrets Manager would incur additional costs and complexity, and it would not leverage the native Kubernetes secrets management capabilities.

Option C is not correct because using the Amazon EBS Container Storage Interface (CSI) driver as an add-on does not encrypt the secrets that are stored in Amazon EKS. The Amazon EBS CSI driver is a plugin that allows you to use Amazon EBS volumes as persistent storage for your Kubernetes pods. It is useful for providing durable and scalable storage for your applications, but it does not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the Amazon EBS CSI driver would require additional configuration and resources, and it would not provide the same level of security as using a KMS key.

Option D is not correct because creating a new AWS KMS key with the alias aws/ebs and enabling default Amazon EBS volume encryption for the account does not encrypt the secrets that are stored in Amazon EKS. The alias aws/ebs is a reserved alias that is used by AWS to create a default KMS key for your account. This key is used to encrypt the Amazon EBS volumes that are created in your account, unless you specify a different KMS key. Enabling default Amazon EBS volume encryption for the account is a setting that ensures that all new Amazon EBS volumes are encrypted by default. However, these features do not affect the encryption of the secrets that are stored in the Kubernetes etcd key-value store. Moreover, using the default KMS key or the default encryption setting would not provide the same level of control and security as using a custom KMS key and enabling the Amazon EKS KMS secrets encryption feature. References:

Encrypting secrets used in Amazon EKS

What Is AWS Key Management Service?

What Is AWS Secrets Manager?

Amazon EBS CSI driver

Encryption at rest

A write-through caching strategy adds or updates data in the cache whenever data is written to the database. This ensures that the data in the cache is always consistent with the data in the database. A write-through caching strategy also reduces the cache miss penalty, as data is always available in the cache when it is requested. However, a write-through caching strategy can increase the write latency, as data has to be written to both the cache and the database. A write-through caching strategy is suitable for applications that require high data consistency and low read latency.

A lazy loading caching strategy only loads data into the cache when it is requested, and updates the cache when there is a cache miss. This can result in stale data in the cache, as data is not updated in the cache when it is changed in the database. A lazy loading caching strategy is suitable for applications that can tolerate some data inconsistency and have a low cache miss rate.

An adding TTL caching strategy assigns a time-to-live (TTL) value to each data item in the cache, and removes the data from the cache when the TTL expires. This can help prevent stale data in the cache, as data is periodically refreshed from the database. However, an adding TTL caching strategy can also increase the cache miss rate, as data can be evicted from the cache before it is requested. An adding TTL caching strategy is suitable for applications that have a high cache hit rate and can tolerate some data inconsistency.

An AWS AppConfig caching strategy is not a valid option, as AWS AppConfig is a service that enables customers to quickly deploy validated configurations to applications of any size and scale. AWS AppConfig does not provide a caching layer for web applications.

References: Caching strategies - Amazon ElastiCache, Caching for high-volume workloads with Amazon ElastiCache

The most suitable design change for the company’s application is to request strongly consistent reads for the table. This change will ensure that the requests to the table return the latest data, reflecting the updates from all prior write operations.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB supports two types of read consistency: eventually consistent reads and strongly consistent reads. By default, DynamoDB uses eventually consistent reads, unless users specify otherwise1.

Eventually consistent reads are reads that may not reflect the results of a recently completed write operation. The response might not include the changes because of the latency of propagating the data to all replicas. If users repeat their read request after a short time, the response should return the updated data. Eventually consistent reads are suitable for applications that do not require up-to-date data or can tolerate eventual consistency1.

Strongly consistent reads are reads that return a result that reflects all writes that received a successful response prior to the read. Users can request a strongly consistent read by setting the ConsistentRead parameter to true in their read operations, such as GetItem, Query, or Scan. Strongly consistent reads are suitable for applications that require up-to-date data or cannot tolerate eventual consistency1.

The other options are not correct because they do not address the issue of read consistency or are not relevant for the use case. Adding read replicas to the table is not correct because this option is not supported by DynamoDB. Read replicas are copies of a primary database instance that can serve read-only traffic and improve availability and performance. Read replicas are available for some relational database services, such as Amazon RDS or Amazon Aurora, but not for DynamoDB2. Using a global secondary index (GSI) is not correct because this option is not related to read consistency. A GSI is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI allows users to query the data in different ways, with eventual consistency3. Requesting eventually consistent reads for the table is not correct because this option is already the default behavior of DynamoDB and does not solve the problem of requests not returning the latest data.

References:

Read consistency - Amazon DynamoDB

Working with read replicas - Amazon Relational Database Service

Working with global secondary indexes - Amazon DynamoDB

This option is the only solution that meets the requirements because it allows the company to encrypt the data with its own encryption keys and tools outside the AWS Cloud. By encrypting the data at the company’s data center before storing the data in the S3 bucket, the company can ensure that the data is encrypted in transit and at rest, and that the company has full control over the encryption keys and processes. This option also avoids the need to use any AWS encryption services or features, which may not be compatible with the company’s security policies or compliance standards.

A. Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) customer managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. Although the company can create and use its own customer managed key in AWS KMS, the key is still stored and managed by AWS KMS, which is a service within the AWS Cloud. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

B. Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) AWS managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default AWS managed key in AWS KMS, which is created and managed by AWS on behalf of the company. The company has no control over the key rotation, deletion, or recovery policies. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

C. Encrypt the data in the S3 bucket with the default server-side encryption (SSE). This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default server-side encryption with Amazon S3 managed keys (SSE-S3), which is applied to every bucket in Amazon S3. The company has no visibility or control over the encryption keys, which are managed by Amazon S3. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company’s security policies or compliance standards.

References:

1 Protecting data with encryption - Amazon Simple Storage Service

2 Protecting data with server-side encryption - Amazon Simple Storage Service

3 Protecting data by using client-side encryption - Amazon Simple Storage Service

4 AWS Key Management Service Concepts - AWS Key Management Service

This solution will meet the requirements because Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports both NFS and SMB protocols, which means it can be accessed by both Linux and Windows applications without code changes. FSx for ONTAP also eliminates data duplication and inefficient resource usage by automatically tiering infrequently accessed data to a lower-cost storage tier and providing storage efficiency features such as deduplication and compression. FSx for ONTAP also integrates with other AWS services such as Amazon S3, AWS Backup, and AWS CloudFormation. By migrating the applications to Amazon EC2 instances, the company can leverage the scalability, security, and performance of AWS compute resources. 

AWS DataSync is a service that makes it easy to move large amounts of data online between on-premises storage and AWS storage services. AWS DataSync can transfer data at speeds up to 10 times faster than open-source tools by using a purpose-built network protocol and parallelizing data transfers. AWS DataSync also handles encryption, data integrity verification, and bandwidth optimization. To use AWS DataSync, users need to deploy a DataSync agent on their on-premises servers, which connects to the NFS servers and syncs the data to Amazon S3. Users can schedule periodic or one-time sync tasks and monitor the progress and status of the transfers.

The other options are not correct because they are either not cost-effective or not suitable for the use case. Setting up AWS Glue to copy the data from the on-premises servers to Amazon S3 is not cost-effective because AWS Glue is a serverless data integration service that is mainly used for extract, transform, and load (ETL) operations, not for simple data backup. Setting up an SFTP sync using AWS Transfer for SFTP to sync data from on premises to Amazon S3 is not cost-effective because AWS Transfer for SFTP is a fully managed service that provides secure file transfer using the SFTP protocol, which is more suitable for exchanging data with third parties than for backing up data. Setting up an AWS Direct Connect connection between the on-premises data center and a VPC, and copying the data to Amazon S3 is not cost-effective because AWS Direct Connect is a dedicated network connection between AWS and the on-premises location, which has high upfront costs and requires additional configuration.

References:

AWS DataSync

How AWS DataSync works

AWS DataSync FAQs

This solution meets the following requirements:

It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.

It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.

It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.

It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

References:

Using AWS Lambda with Amazon S3 - AWS Lambda

What Is Amazon Comprehend? - Amazon Comprehend

Working with Tables - Amazon DynamoDB