Amazon Web Services SAA-C03 Dumps

AWS Backup is the most appropriate solution for managing backups with minimal operational overhead while meeting the regulatory requirement to retain data for 90 days and enabling point-in-time restore for up to 14 days.

AWS Backup: AWS Backup provides a centralized backup management solution that supports automated backup scheduling, retention management, and compliance reporting across AWS services, including Amazon RDS. By creating a backup plan, you can define a retention period (in this case, 90 days) and automate the backup process.

Point-in-Time Restore (PITR): Amazon RDS supports point-in-time restore for up to 35 days with automated backups. By using AWS Backup in conjunction with RDS, you ensure that your backup strategy meets the requirement for restoring data to a specific point in time within the last 14 days.

Why Not Other Options?:

Option A (RDS Automated Backups): While RDS automated backups support PITR, they do not directly support retention beyond 35 days without manual intervention.

Option B (Manual Snapshots): Manually creating and managing snapshots is operationally intensive and less automated compared to AWS Backup.

Option C (Aurora Clones): Aurora Clone is a feature specific to Amazon Aurora and is not applicable to Amazon RDS for Oracle.

AWS References:

AWS Backup- Overview of AWS Backup and its capabilities.

Amazon RDS Automated Backups- Information on how RDS automated backups work and their limitations.

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

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 Arequires the management of IAM temporary credentials, adding complexity.

Option Cinvolves unnecessary development effort.

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

For Amazon RDS relational databases experiencing read-heavy workloads, AWS best practice is to create read replicas and offload read traffic to those replicas. A read replica:

Uses asynchronous replication from the primary.

Can serve read-only queries, thereby reducing load and query latency on the primary.

Can be used behind an application-level read/write splitting mechanism or via endpoints that direct read traffic to replicas.

Performance Insights (Option A) helps diagnose performance problems but does not itself offload traffic.

DAX (Option B) is a cache for DynamoDB, not RDS.

Kinesis Data Firehose (Option D) is a streaming ingestion service and cannot increase RDS query concurrency.

The most secure and least operationally intensive method is to configure cross-account access using resource-based policies on the S3 bucket. This allows trusted external AWS accounts (consulting agencies) to securely access the S3 data without the need to manage user credentials or build additional infrastructure.

Options A and B pose security risks. Option D increases operational complexity and violates least privilege by managing external users inside your AWS account.

The most secure solution for allowing EC2 instances to access an S3 bucket is by usingIAM 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 anIAM 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 Instancesoutlines the use of IAM roles for secure access to AWS services.

AWS CloudFormation User Guidedetails 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.

SSE-KMS (Server-side encryption with AWS Key Management Service) not only encrypts data at rest but also integrates with AWS CloudTrail to provide detailed logs of key usage — meeting the audit requirement.

“SSE-KMS provides the ability to audit key usage to see who used the key and when, via AWS CloudTrail.”

— Amazon S3 Encryption Documentation

Benefits:

Encryption with customer-managed or AWS-managed KMS keys

Audit trails of key usage events

Fine-grained access control

Incorrect Options:

A: SSE-S3 does not support auditing of key usage.

C: SSE-C does not integrate with CloudTrail or KMS.

D: Self-managed keys require external key infrastructure and custom audit logging.

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. To scale across Regions and accounts in AWS Organizations, Macie supports delegated administration, automated sensitive data discovery, and multi-account aggregation through a centralized admin account.

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables

Explanation (AWS Docs):

To allow private subnets to access the internet, deploy NAT gateways in a public subnet in each AZ for high availability. NAT instances are less scalable and less fault-tolerant.

“To create a highly available architecture, create a NAT gateway in each Availability Zone and configure your routing to use it.”

— NAT Gateway Overview

For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

Amazon EFS Lifecycle Management enables automatic cost optimization by transitioning files that haven’t been accessed for a defined period (e.g., 7 days) from EFS Standard to EFS Infrequent Access (IA).

“Amazon EFS Lifecycle Management automatically moves files that haven’t been accessed for a set period to the EFS Infrequent Access storage class, reducing storage costs for infrequently accessed files.”

— Amazon EFS Documentation

Key Points:

EFS IA is ideal for files larger than 128 KB and accessed less frequently.

It’s seamless — no code or tools needed.

Meets requirement for cost optimization and high availability.

Incorrect Options:

A: File Gateway adds unnecessary complexity and does not use EFS.

B: Storing files locally breaks shared access and resiliency.

D: Glacier Deep Archive is cold storage — not "readily available."

Amazon DynamoDB supports both TTL (Time to Live) for automatic deletion and Point-in-Time Recovery (PITR) for backup and restore of table data. It's ideal for frequently updated data with short retention requirements and minimal operational overhead.

TTL attribute ensures expired items are automatically removed.

PITR enables recovery from accidental deletes or application errors.

To achieve high availability for the website, two key actions should be taken:

Amazon RDS for MySQL Multi-AZ: By migrating the database to an RDS for MySQL Multi-AZ deployment, the database becomes highly available. Multi-AZ provides automatic failover from the primary database to a standby replica in another Availability Zone, ensuring database availability even in the case of an AZ failure.

Application Load Balancer and Auto Scaling: Deploying an Application Load Balancer (ALB) in front of the EC2 instances ensures that traffic is evenly distributed across the instances. Configuring an Auto Scaling group to run EC2 instances across multiple Availability Zones ensures that the application remains available even if one instance or one AZ becomes unavailable. This setup enhances fault tolerance and improves reliability.

Why Not Other Options?:

Option A (Internet Gateway per AZ): Internet Gateways are region-wide resources and do not need to be provisioned per Availability Zone. This option does not contribute to high availability.

Option C (DynamoDB + Auto Scaling): DynamoDB would require changes to the application code to switch from MySQL, which is not possible per the question's constraints.

Option D (DataSync): AWS DataSync is used for data transfer and synchronization, not for achieving high availability for a database.

AWS References:

Amazon RDS Multi-AZ Deployments- Explanation of how Multi-AZ deployments work in Amazon RDS.

Application Load Balancing- Details on how to configure and use ALB for distributing traffic across multiple instances.

For an application requiring TCP communication and high availability:

Network Load Balancer (NLB)is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling groupensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.

AWS Lambda is event driven and “scales automatically with the number of requests,” charging per request and compute duration in milliseconds; you can schedule with Amazon EventBridge rules. Allocating 1 GB memory proportionally increases available CPU, which suits short CPU bursts. For a 10-second, once-per-hour job, Lambda eliminates instance idle time, providing the lowest cost and no servers to manage. Fargate tasks (A) bill per-second with a 1-minute minimum and require container packaging; for a 10-second job, costs are higher than Lambda. Options C and D keep EC2 management overhead and either maintain instances or add start/stop orchestration complexity; EC2 costs dominate because the instance sits idle for 59+ minutes each hour. Therefore, Lambda + EventBridge is the most cost-effective and operationally simple solution.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

Comprehensive Explanation:Deploying the frontend as a CloudFront distribution with multiple origins provides an efficient and scalable solution. Using WAF rules with CloudFront protects against web vulnerabilities, while the multi-origin configuration allows traffic routing to the on-premises backend APIs. This approach minimizes operational overhead compared to managing EC2 instances.

Key Problem:

Delayed Inventory table updates during peak sales.

DynamoDB Streams and Lambda processing require optimization.

Analysis of Options:

Option A:Adding a GSI is unrelated to the issue. It does not address stream processing delays or capacity issues.

Option B:Optimizing batch size reduces latency and allows the Lambda function to process larger chunks of data at once, improving performance during peak load.

Option C:Increasing write capacity for the Inventory table ensures that it can handle the increased volume of updates during peak times.

Option D:Increasing read capacity for the Orders table does not directly resolve the issue since the problem is with updates to the Inventory table.

Option E:Increasing Lambda timeout only addresses longer processing times but does not solve the underlying throughput problem.

AWS References:

DynamoDB Streams Best Practices

Provisioned Throughput in DynamoDB

AWS Config allows for creation of custom rules to monitor EBS configurations. Combined with CloudWatch metrics and Amazon SNS, custom rules can track over-provisioned IOPS and send alerts when thresholds are breached, allowing proactive cost and performance management.

Why Option C is Correct:

NAT Gateway: Allows private subnets to access the internet for outbound requests while preventing inbound connections.

High Availability: Deploying NAT gateways in both AZs ensures fault tolerance.

Shared Route Table: Simplifies routing configuration for private subnets.

Why Other Options Are Not Ideal:

Option A: Creating separate route tables for each subnet adds unnecessary complexity.

Option B: Internet gateways allow inbound access, violating the requirement to block public IPv4 access.

Option D: Egress-only internet gateways are designed for IPv6, not IPv4.

AWS References:

Amazon VPC NAT Gateway:AWS Documentation - NAT Gateway

AWS Shield Advanced provides:

• 24/7 access to the AWS DDoS Response Team (DRT)

• Near real-time attack visibility and metrics

• Protection for CloudFront, ALB, NLB, and Route 53

Shield Standard (Option B) does not include DDoS response team support.

WAF (Option A) provides rules-based filtering, not DDoS assistance.

Macie (Option C) is for PII discovery, not DDoS protection.

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This question centers on improving scalability and global access performance.

Auto Scaling groups enable EC2 instances to scale dynamically in response to demand, ensuring availability during peak hours without manual intervention. Amazon RDS read replicas offload read traffic, improving read throughput and reducing latency on the primary database instance. Deploying Amazon CloudFront with S3 as origin accelerates delivery of static transaction documents globally by caching content at edge locations, reducing latency for users worldwide.

Option B focuses on vertical scaling (larger instances) and caching with ElastiCache, but it does not address global content delivery optimally. AWS Global Accelerator accelerates network traffic but is better suited for accelerating TCP and UDP traffic; CloudFront is generally preferred for HTTP content delivery.

Option C migrates workloads to Lambda and Aurora global databases, which is an advanced and potentially costly redesign that may not be necessary. Option D suggests moving to ECS and multi-AZ RDS but does not address global content delivery efficiently.

Therefore, option A uses proven scalability and caching best practices aligned with AWS Well-Architected Framework pillars for performance and operational excellence.

Amazon FSx for NetApp ONTAP supports Multi-AZ, providing automatic failover between Availability Zones for high availability. It exposes ONTAP LUNs over the iSCSI protocol, delivering shared block storage semantics with low latency and consistent performance to EC2 clients across AZs. This meets the requirement for “highly available” and “low-latency” block access across multiple AZs. S3/S3 File Gateway (A) is object/file, not block storage. EBS (B, D) provides block storage to a single instance in a single AZ; EBS volumes cannot be shared across instances/AZs, and host-side sync scripts add latency, complexity, and do not provide true HA. FSx for ONTAP natively provides synchronous HA pair replication, fast failover, and supports iSCSI multipathing for resilient, performant access suited to latency-sensitive trading workloads.

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

An Application Load Balancer supports path- and host-based routing, which makes it ideal for routing requests based on subdomains. EC2 Auto Scaling ensures that the number of instances adjusts dynamically based on traffic, which helps manage cost and performance during predictable peak hours.

Amazon S3 uses gateway VPC endpoints, which enable private, secure access to S3 without traversing the internet, compatible with IPv6.

Amazon DynamoDB uses interface VPC endpoints (powered by AWS PrivateLink) for private connectivity within the VPC.

Therefore, for secure private communication without public internet, the correct solution is to implement a gateway VPC endpoint for S3 and an interface VPC endpoint for DynamoDB.

Option B is incorrect because S3 does not support interface endpoints; Option C is incorrect because DynamoDB does not support gateway endpoints. Option D reverses the correct endpoint types.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

AWS Backup Audit Manager automates auditing and reporting of backup activity and compliance, while AWS Config provides visibility into configuration changes. Together, they provide the simplest, most automated, and compliant backup monitoring solution.

From AWS Documentation:

“AWS Backup Audit Manager automatically audits backup activity across AWS resources. You can use predefined or custom frameworks to monitor backup compliance and encryption status.”

(Source: AWS Backup Audit Manager User Guide)

Why D is correct:

Ensures centralized visibility into all backup jobs.

Verifies encryption status automatically.

Generates ready-to-use reports with minimal operational overhead.

Complies with regulatory requirements for data protection.

Why others are incorrect:

A & C: Custom Lambda automation increases maintenance effort.

B: Manual checking is operationally inefficient and error-prone.

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.

AWS Security Hub provides a centralized view of security findings across AWS accounts and services. It integrates natively with AWS Config conformance packs, which evaluate compliance against industry standards such as CIS and PCI-DSS.

From AWS Documentation:

“AWS Security Hub aggregates, organizes, and prioritizes security alerts and compliance status across AWS accounts. Use AWS Config conformance packs to assess compliance with security frameworks.”

(Source: AWS Security Hub User Guide – Managing Findings and Compliance)

Why C is correct:

Security Hub provides a centralized dashboard for compliance visibility.

Conformance packs in AWS Config automate compliance checks across accounts.

Fully managed, minimal maintenance, and integrates natively with AWS services.

Why others are incorrect:

A: Conformance packs are not a feature of Amazon Inspector.

B: Third-party tools on EC2 require management and add operational overhead.

D: Systems Manager is not designed for compliance aggregation.

Serving static content from Amazon S3 is highly cost-effective and scalable. By fronting the S3 bucket with Amazon CloudFront, you also enable global caching, reduced latency, and even lower costs by reducing direct S3 access. There is no need for EC2 or ALB, and no need to manage servers, further lowering operational burden and expenses. This pattern is the recommended AWS architecture for serving static web content.

Reference Extract from AWS Documentation / Study Guide:

"Hosting static websites on Amazon S3 with Amazon CloudFront reduces infrastructure costs and improves performance for global users by caching content at edge locations."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 and CloudFront section.

Explanation (AWS Docs):

Although the question says “before sending it,” AWS best practice for sensitive data is SSE-KMS (Server-side encryption with AWS KMS keys), which gives full key usage auditing. It integrates with AWS KMS and provides compliance-friendly encryption at rest automatically.

“SSE-KMS uses AWS Key Management Service to manage encryption keys. SSE-KMS also provides an audit trail of key usage.”

— Protecting Data Using Server-Side Encryption

Why not D?

Client-side encryption requires custom key management and adds operational overhead. C is simpler and compliant.

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.

Since the workload is interruptible and can resume after restarts, Amazon EC2 Spot Instances are the most cost-effective option, offering savings of up to 90% compared to On-Demand. Running the batch workload on AWS Batch with Spot Instances allows automatic job queue management, interruption handling, and scheduling. Option A and C reduce cost but still rely on reserved or committed pricing for On-Demand capacity. Option B (Capacity Reservations) increases cost instead of reducing it. Therefore, the most cost-optimized solution is D — AWS Batch with EC2 Spot Instances.

The correct and secure approach is to use Amazon CloudFront with Origin Access Control (OAC) to protect the S3 origin and attach AWS WAF to the CloudFront distribution to inspect and filter traffic at the edge before reaching the origin.

From AWS Documentation:

“AWS WAF is integrated with Amazon CloudFront, allowing inspection of HTTP(S) requests at the edge location before forwarding to your origin. To restrict direct access to the S3 bucket, use Origin Access Control (OAC).”

(Source: Amazon CloudFront Developer Guide – Serving private content)

Why Option D is correct:

CloudFront is the only service that integrates with AWS WAF for full HTTP layer inspection.

Origin Access Control (OAC) ensures that only CloudFront can access the S3 origin—replacing older Origin Access Identity (OAI) features.

The S3 bucket policy is configured to trust requests only from CloudFront using OAC signed requests.

Why the other options are incorrect:

Option A: WAF ARN is not a principal in S3 bucket policy. IAM does not support bucket policies based on WAF ARNs.

Option B: Incorrect – CloudFront doesn't "forward requests to WAF"; rather, WAF is associated with CloudFront and inspects requests at the edge.

Option C: S3 does not use security groups; they are for EC2/network interfaces. This shows a misunderstanding of how S3 works.

Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

To serve a static website hosted in Amazon S3 over HTTPS, you must use Amazon CloudFront because S3 does not natively support HTTPS for static website endpoints.

Steps to meet HTTPS requirement:

B. Create a CloudFront distribution and configure the S3 bucket as the origin. This enables global edge caching and performance optimization.

D. Attach an SSL/TLS certificate (typically from AWS Certificate Manager) to the CloudFront distribution to handle HTTPS connections.

S3 buckets used as static website hosts only support HTTP directly. While S3 supports HTTPS for REST API access, it does not support HTTPS on static website endpoints.

This setup aligns with security best practices and supports the Secure and Operational Excellence pillars of the AWS Well-Architected Framework.

???? References:

Hosting a static website using Amazon S3 and CloudFront

CloudFront + HTTPS with ACM

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn't traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it's over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn't meet the low-latency requirements of the use case.

To authenticate users using an on-premises Active Directory Federation Service (AD FS), AWS provides the AWS Directory Service AD Connector. AD Connector is a proxy that connects AWS applications to your on-premises Microsoft Active Directory without requiring complex directory synchronization or the need to set up a separate directory in the cloud.

By integrating AD Connector with the Application Load Balancer (ALB), you can authenticate and authorize users using your existing on-premises credentials. This setup allows the ALB to leverage the authentication capabilities of your on-premises AD FS, providing a seamless and secure user experience.

AWS Fargate provides per-pod isolation for CPU, memory, storage, and networking, making it ideal for multi-tenant use cases.

AWS Documentation References:

EKS with Fargate

To optimize costs for both Amazon EC2 and AWS Fargate, the best option is a Compute Savings Plan because it offers flexibility across instance families, Regions, and compute options including EC2, AWS Fargate, and AWS Lambda.

Unlike EC2 Instance Savings Plans, which apply only to specific instance families, Compute Savings Plans apply across multiple services.

Since the company does not want to commit to multi-year contracts or large upfront payments, the 1-year No Upfront Compute Savings Plan provides the greatest flexibility with no upfront capital commitment, while still offering cost savings over On-Demand pricing.

This option also aligns with cost-optimization best practices by allowing for scalability and service mix flexibility.

???? Reference:

AWS Compute Savings Plans

AWS Pricing Models

Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.

Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.

Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.

Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.

Amazon Timestream Documentation

TheGateway 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​.

The optimal solution for scalable and resilient hybrid networking is to use AWS Direct Connect with a Direct Connect gateway for secure, low-latency access to AWS, and an AWS Transit Gateway to manage connectivity among hundreds of VPCs.

By associating the Transit Gateway with the Direct Connect gateway, you enable transitive routing between on-premises and all VPCs, while minimizing network complexity and maintaining high performance.

VPC peering does not scale well, and VPNs don’t offer the same performance or consistency.

Application Load Balancer (ALB) supports HTTP/HTTPS layer 7 routing, including header-based routing, path-based routing, and host-based routing.

AWS WAF is designed to provide rules-based filtering (block, allow, count) of HTTP(S) requests to protect applications from common exploits and malicious traffic.

ALB integrates directly with AWS WAF, so you can attach a web ACL with custom rules defined by the security team to block specific patterns while still using header-based routing.

Why the others are not correct:

A: Mutual TLS adds client certificate authentication but does not provide rules-based blocking or WAF-style inspection.

C: AWS Config is for configuration compliance and auditing, not request filtering.

D: NLB operates at layer 4 (TCP/UDP); it does not support HTTP header-based routing.

AWS WAF allows web applications to protect themselves from common web exploits and vulnerabilities. Using AWS managed rule groups ensures protection against known attack patterns, such as SQL injection and cross-site scripting. Associating AWS WAF with the ALB provides application-layer security and real-time threat mitigation.

Explanation (AWS Docs):

You cannot “place” DynamoDB inside a VPC. Instead, you use a VPC endpoint.

A Gateway VPC Endpoint for DynamoDB enables private connectivity between your VPC and DynamoDB without traversing the public internet.

“Use a gateway VPC endpoint to privately connect your VPC to DynamoDB without requiring an internet gateway or NAT.”

— Gateway VPC Endpoints

Comprehensive and Detailed Step-by-Step Explanation:

The company needsautomatic monitoringandnotificationsforsecurity violationsin Amazon Redshift clusters.

Option A:❌

Create an Amazon EventBridge rule that uses the Redshift clusters as the source. Create an AWS Lambda function to evaluate the Redshift cluster security configuration. Configure the Lambda function to notify the company of any violations of the security policies. Add the Lambda function as a target of the EventBridge rule.

EventBridgecan trigger actionsbased on events.

However, itdoes not track changesin Redshift security configurations.

Why not?AWS Configis bettersuited forcompliance monitoring.

This question focuses on scalability, operational overhead, and performance during unpredictable workloads.

API Gateway + AWS Lambda enables serverless compute, which scales automatically based on the number of requests. It requires no provisioning, maintenance, or patching of servers — eliminating operational overhead.

Amazon DynamoDB is a fully managed NoSQL database optimized for high-throughput workloads with single-digit millisecond latency.

Amazon S3 is designed for high availability and durability, and is ideal for storing unstructured content such as user-uploaded images.

By leveraging these fully managed and scalable services, the architecture meets the requirement of supporting rapid user growth while minimizing operational complexity. This solution aligns with the Performance Efficiency and Operational Excellence pillars in the AWS Well-Architected Framework.

???? References:

Serverless Web Application Architecture

Using DynamoDB with Lambda

Best Practices for API Gateway

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.

Amazon RDS Proxy: RDS Proxy is a fully managed, highly available database proxy that makes applications more resilient to database failures by pooling and sharing connections, and it can automatically handle database failovers.

Reduced Failover Time: By using RDS Proxy, the connection management between the application and the database is improved, reducing failover times significantly. RDS Proxy maintains connections in a connection pool and reduces the time required to re-establish connections during a failover.

Configuration:

Create an RDS Proxy instance.

Configure the proxy to connect to the RDS for PostgreSQL DB instance.

Modify the application configuration to use the RDS Proxy endpoint instead of the direct database endpoint.

Operational Benefits: This solution provides high availability and reduces application timeouts during failovers with minimal changes to the application code.

BothAmazon KinesisandSQS FIFOqueues ensure the sequential processing of messages. By using the payment ID as the partition key in Kinesis or as the message group in the SQS FIFOqueue, messages are processed in order. Both solutions also allow for long-term retention (up to 10 days) of messages, making them suitable for this payment processing use case.

Option A (DynamoDB): DynamoDB does not guarantee message ordering for real-time processing.

Option C (ElastiCache): ElastiCache is for caching, not suitable for sequential message processing.

Option D (Standard SQS queue): A standard SQS queue does not guarantee ordering of messages.

AWS References:

Amazon Kinesis

Amazon SQS FIFO Queues

Key Requirements:

Host the frontend on AWS as a static website.

Protect the application from common web vulnerabilities.

Minimal operational overhead.

Analysis of Options:

Option A:

Hosting the frontend on EC2 with an ALB introduces unnecessary complexity for serving static content.

AWS WAF rules can protect the ALB, but managing EC2 instances adds operational overhead.

Incorrect Approach:High operational complexity for a simple static website.

Option B:

Amazon CloudFront:Acts as a global CDN, reducing latency and protecting against DDoS attacks.

Multiple Origins:Allows static content to be served from S3 while routing API traffic to the on-premises backend.

AWS WAF:Integrates with CloudFront to provide web application protection.

Correct Approach:Offers low operational overhead with optimal security and performance.

Option C:

Using NLB and Network Firewall is unnecessary for a static website. This approach increases cost and complexity without addressing the frontend requirements effectively.

Incorrect Approach:Over-engineered solution.

Option D:

Hosting the frontend on S3 and using API Gateway is a viable option, but managing AWS WAF rules separately for both the S3 bucket and the REST API increases complexity.

Incorrect Approach:Less efficient than using CloudFront with multiple origins.

AWS Solution Architect References:

Amazon CloudFront Overview

AWS WAF with CloudFront

Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability. Option A (RDS) would not handle massive scale or traffic spikes as efficiently. Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose. Option D (DocumentDB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.

Amazon GuardDuty detects cryptocurrency mining and sends findings to Amazon EventBridge. You can use EventBridge to trigger an automated Lambda function to isolate EC2 instances (such as by removing security group access or stopping/isolating the instance).

AWS Documentation Extract:

"Amazon GuardDuty findings can be sent to Amazon EventBridge, which enables you to trigger an automated response using AWS Lambda."

(Source: AWS GuardDuty documentation)

B, C, D: Security Hub, Inspector, and Config are not directly used for this detection-to-isolation workflow.

AWS Global Accelerator is designed to improve the availability and performance of applications with global users by using the AWS global network. It provides static anycast IP addresses and routes user traffic over the AWS edge network to the optimal AWS Region and endpoint based on health, geography, and routing policies. Global Accelerator supports both TCP and UDP traffic and can have Network Load Balancers as endpoints.

For latency-sensitive workloads such as multiplayer gaming, Global Accelerator reduces latency and jitter compared to internet-based routing and handles Regional failover quickly.

CloudFront (Option A) is optimized for HTTP/HTTPS content caching and is not appropriate for arbitrary TCP/UDP gaming traffic. Application Load Balancers (Option B) do not support UDP traffic. API Gateway (Option D) is for HTTP APIs and is not suitable for raw TCP/UDP game traffic.

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.

For workloads where tasks are independent and can be processed in parallel, and where minimizing management overhead is a priority, a serverless architecture using AWS Lambda is ideal.

AWS Lambda allows you to run code without provisioning or managing servers. It automatically scales your application by running code in response to each trigger.

Parallel Processing: Lambda functions can process multiple tasks concurrently, making it suitable for parallel workloads.

Automatic Scaling: Lambda automatically scales by running code in response to each event, scaling precisely with the size of the workload.

Minimal Management Overhead: With Lambda, there's no need to manage the underlying infrastructure, reducing operational complexity.Wikipedia

Amazon S3 supports one Lifecycle configuration per bucket that can contain multiple rules. Lifecycle rules can include filters, including object size filters: ObjectSizeGreaterThan and ObjectSizeLessThan. You can combine rules so that one targets large objects and another provides a default expiration. Here, configure Rule 1 with a size filter ObjectSizeGreaterThan = 1 TB and Expiration = 2 days (48 hours) to purge very large videos early. Configure Rule 2 with Expiration = 7 days without a size filter to serve as a catch-all maximum retention for all objects. Options B and D are invalid because S3 does not allow multiple Lifecycle configurations per bucket. Option C cannot distinguish large files; it would delete all files at the same schedule without honoring the 48-hour policy for >1 TB objects. This single configuration with two rules meets both retention targets with minimal overhead and full S3-native capability.

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

Option Buses SNS, which does not maintain message order.

Option Cis suitable for serverless workflows but not relational data.

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

AWS Control Tower: Provides a managed service to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of AWS Organizations and applies security controls (guardrails).

Networking Account:

Create a centralized networking account that includes a VPC with both private and public subnets.

This centralized VPC will manage and control the networking resources.

AWS Resource Access Manager (AWS RAM):

Use AWS RAM to share the subnets from the networking account with the other workload accounts.

This allows different workload accounts to utilize the shared networking resources without the need to manage their own VPCs.

Operational Efficiency: Using AWS Control Tower simplifies the setup and governance of multiple AWS accounts, while AWS RAM facilitates centralized management of networking resources, reducing operational overhead and ensuring consistent security and compliance.

Lambda supports mounting an Amazon EFS file system inside your function to store larger dependencies beyond the 250 MB layer quota.

EFS automatically encrypts data in transit using TLS.

“You can configure your Lambda function to mount an Amazon EFS file system, enabling your function to access large amounts of data or large dependencies.”

“Amazon EFS automatically encrypts all data at rest and in transit.”

— Lambda with Amazon EFS

This is the least operational overhead approach.

The AWS Cost and Usage Report (CUR) delivers detailed, line-item billing data to Amazon S3. AWS recommends querying CUR with Amazon Athena by creating external tables over the CUR S3 location (partitioned by time) to produce daily cost aggregations such as NAT Gateway (EC2:NatGateway) usage and cost. Amazon QuickSight natively connects to Athena as a data source to build and share dashboards with visuals (tables, time series) filtered from the start of the current month. DataSync (A, C) is a file transfer service and cannot query data. CloudWatch dashboards (C, D) visualize metrics/logs, not CUR datasets. Therefore, using Athena to query CUR and QuickSight to present a daily NAT gateway cost dashboard is the most direct and operationally efficient approach.

Amazon FSx for Lustre is a high-performance parallel file system designed for machine learning and HPC that can be linked to Amazon S3 via Data Repository Associations (DRA). With DRA, you can import S3 objects as files (lazy or preloaded) and export results back to S3, providing very high throughput and low-latency POSIX access on the training cluster. This S3-native integration minimizes data loading overhead and accelerates training cycles at scale. EFS (A) is general-purpose and lower throughput per TB than Lustre. EBS (C) requires manual copy and does not scale as a shared parallel filesystem. DataSync alone (D) is a transfer tool, not a high-throughput training filesystem. FSx for Lustre with S3 DRA best satisfies scalability, throughput, and native S3 integration for rapid ML training.

S3 One Zone-IA:

Suitable for infrequently accessed data that doesn't require multiple Availability Zone resilience.

Cost-effective for storing user-uploaded images that are only used for AI model training twice a year.

S3 Standard:

Ideal for frequently accessed data with high durability and availability.

Store premium user-generated AI images here to ensure they are readily available for download at any time.

S3 Standard-IA:

Cost-effective storage for data that is accessed less frequently but still requires rapid retrieval.

Store non-premium user-generated AI images here, as these images are only downloaded once every 6 hours, making it a good balance between cost and accessibility.

Cost-Effectiveness: This solution optimizes storage costs by categorizing data based on access patterns and durability requirements, ensuring that each type of data is stored in the most cost-effective manner.

AWS Secrets Manager is designed specifically to securely store and manage sensitive information such as database credentials. It integrates seamlessly with AWS services like Lambda and RDS, and it provides automatic credential rotation with minimal operational overhead.

AWS Secrets Manager: By storing the database credentials in Secrets Manager, you ensure that the credentials are securely stored, encrypted, and managed. Secrets Manager provides a built-in mechanism to automatically rotate credentials at regular intervals (e.g., every 30 days), which helps in maintaining security best practices without requiring additional manual intervention.

Lambda Integration: The Lambda function can be easily configured to retrieve the credentials from Secrets Manager using the AWS SDK, ensuring that the credentials are accessed securely at runtime.

Why Not Other Options?:

Option A (Parameter Store with Rotation): While Parameter Store can store parameters securely, Secrets Manager is more tailored for secrets management and automatic rotation, offering more features and less operational overhead.

Option C (Encrypted Lambda environment variable): Storing credentials directly in Lambda environment variables, even when encrypted, requires custom code to manage rotation, which increases operational complexity.

Option D (KMS with automatic rotation): KMS is for managing encryption keys, not for storing and rotating secrets like database credentials. This option would require more custom implementation to manage credentials securely.

AWS References:

AWS Secrets Manager- Detailed documentation on how to store, manage, and rotate secrets using AWS Secrets Manager.

Using Secrets Manager with AWS Lambda- Guidance on integrating Secrets Manager with Lambda for secure credential management.

Amazon Macieis purpose-built for detecting PII in S3.

Option Auses EventBridge to filter SensitiveData findings and notify via SNS, meeting the requirements.

Options B and Dinvolve GuardDuty, which is not designed for PII detection.

Option Cuses SQS, which is less suitable for immediate notifications.

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

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.

AWS Secrets Manager is a fully managed service specifically designed to securely store and automatically rotate database credentials, API keys, and other secrets. Secrets Manager provides built-in integration with Amazon RDS for automatic credential rotation on a configurable schedule without requiring downtime. It also manages the secure distribution of the credentials to authorized services, such as your web servers, using IAM policies. Manual solutions (S3, files, cron jobs) do not provide the same level of automation, audit, or security.

Reference Extract from AWS Documentation / Study Guide:

"AWS Secrets Manager enables you to rotate, manage, and retrieve database credentials securely. It supports automatic rotation of secrets for supported AWS databases without requiring application downtime."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and Secrets Management section.

Amazon Elastic File System (EFS) is the ideal solution for migrating legacy applications that require NFS (Network File System) communication. EFS provides fully managed, scalable NFS storage in the cloud, and it supports the standard NFS protocols, allowing the legacy application to continue using NFS without modification after migration to AWS.

Key AWS features:

NFS Support: EFS natively supports the NFSv4 protocol, which makes it the best solution for workloads that rely on NFS communication.

Scalability and Availability: EFS automatically scales as application demands grow, making it a highly available and reliable storage solution.

AWS Documentation: According to AWS’s best practices for file storage, EFS is recommended for any workloads requiring NFS support in a cloud environment​​.

For globally distributed consumers of REST APIs, API Gateway edge-optimized endpoints “route requests to the nearest CloudFront edge location, which then forwards them to your API in the [home] Region,” reducing latency for users worldwide and scaling automatically for unknown or spiky traffic. By contrast, Regional endpoints are intended for clients within the same or nearby Region and do not provide global edge acceleration. AWS Lambda provides automatic scaling for serverless backends; latency can be further reduced by right-sizing memory (which also increases CPU) and, when needed, enabling provisioned concurrency to keep functions initialized and eliminate cold starts for critical paths. Using NLBs across Regions is not serverless and adds operational complexity without CloudFront edge acceleration. Therefore, combining API Gateway edge-optimized REST APIs with Lambda meets the requirements of minimal global latency and unknown initial traffic.

Amazon EC2 allows you to install and manage SQL Server directly on a Windows or Linux VM, giving you full access to the underlying operating system. This is required when you need to install custom agents, configure OS-level features, or have complete control over the environment.

Amazon RDS and Aurora are managed services and do not provide access to the underlying OS, nor does Redshift (which is a data warehouse, not SQL Server).

AWS Documentation Extract:

"If you require access to the underlying operating system or need to install custom software, use SQL Server on Amazon EC2. Amazon RDS is a managed service and does not provide OS-level access."

(Source: AWS Database Migration documentation, SQL Server Deployment Options)

AmazonS3 with the Standard storage classis the best solution:

Encryption: SSE-S3 ensures server-side encryption of the data, meeting compliance requirements.

Immediate access: The Standard storage class provides low-latency and high-throughput access to data.

Multi-location storage: Cross-Region replication ensures data is stored in multiple AWS Regions for redundancy.

Why Other Options Are Not Ideal:

Option B:

Amazon EFS is more costly and suited for file systems rather than object storage.Not cost-effective.

Option C:

Amazon EBS is block storage and not optimized for object storage like PDFs. Backup schedules do not ensure immediate availability.Not suitable.

Option D:

S3 Glacier Flexible Retrieval is designed for archival, not immediate access.Does not meet access requirements.

AWS References:

Amazon S3 Standard Storage:AWS Documentation - S3 Storage Classes

Amazon S3 Cross-Region Replication:AWS Documentation - Cross-Region Replication

AWS Encryption Options:AWS Documentation - S3 Encryption

AWS Shield Advanced provides advanced DDoS protection for AWS workloads, including EC2, CloudFront, and RDS. When integrated with CloudFront, Shield Advanced offers comprehensive detection and mitigation against large and sophisticated DDoS attacks, along with 24x7 access to the AWS DDoS Response Team (DRT). AWS WAF provides application-level protection, but for complete DDoS mitigation, Shield Advanced is the recommended solution.

Reference Extract from AWS Documentation / Study Guide:

"AWS Shield Advanced provides expanded DDoS attack protection for applications running on AWS. It offers always-on detection and automatic inline mitigations that minimize application downtime and latency."

Source: AWS Certified Solutions Architect – Official Study Guide, Security and DDoS Protection section.

S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

"S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable."

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

AWS Global Accelerator provides static anycast IP addresses that remain constant regardless of Regional failover. AWS directs traffic to the optimal healthy Region without relying on DNS TTL values, eliminating the risk of stale DNS caches.

Route 53 routing (Options B and D) still depends on DNS caching behavior. CloudFront (Option A) is for content delivery, not Regional failover for applications.

=====================================================

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

Aurora I/O-Optimized: This storage configuration is designed to provide consistent high performance for Aurora databases. It automatically scales IOPS as the workload increases, without needing to provision IOPS separately.

Cost-Effectiveness: With Aurora I/O-Optimized, you only pay for the storage and I/O you use, making it a cost-effective solution for applications with varying and unpredictable I/O demands.

Implementation:

During the creation of the Aurora PostgreSQL Serverless v2 cluster, select the I/O-Optimized storage configuration.

The storage system will automatically handle scaling and performance optimization based on the application load.

Operational Efficiency: This configuration reduces the need for manual tuning and ensures optimal performance without additional administrative overhead.

AmazonKinesis Data Streamsis the best choice for this scenario:

Message throughput: Kinesis Data Streams supports high throughput with enhanced fan-out and dedicated throughput for consumers.

Large message size: Supports message sizes up to 1 MB, meeting the 500 KB requirement.

Message retention: Data streams can retain messages for up to 365 days.

Strict ordering: Guarantees message ordering within shards.

Why Other Options Are Not Ideal:

Option B:

While SQS FIFO supports strict ordering, SNS topics do not. SNS also does not natively support message retention or strict ordering across consumers.Does not meet requirements.

Option C:

EventBridge does not provide strict ordering guarantees or message retention beyond 24 hours.Does not meet requirements.

Option D:

SNS topics with Data Firehose are not designed for use cases requiring strict ordering or long message retention.Does not meet requirements.

AWS References:

Amazon Kinesis Data Streams:AWS Documentation - Kinesis Data Streams

AWS Messaging Services Comparison:AWS Documentation - Messaging Services

AWS Global Accelerator improves the performance and availability of applications by directing user traffic through the AWS global network of edge locations using anycast IP addresses. It reduces latency and jitter for global users accessing applications in a single Region.

Why this works:

Global Accelerator routes user requests to the nearest AWS edge location using AWS’s high-performance backbone network.

It then forwards traffic to the optimal endpoint — in this case, the public API hosted on EC2.

This is much more cost-effective and requires less operational complexity than deploying and maintaining multiple API Gateway endpoints across regions (Option B), or setting up Direct Connect links for every international location (Option A).

Option C requires no application change and is designed specifically for latency improvement and high availability.

???? References:

AWS Global Accelerator Documentation

Use Cases for Global Accelerator

Performance Improvements for Global Users

To improve read performance for a MySQL-based RDS database under load, you can:

Use Read Replicas: Amazon RDS supports MySQL read replicas, which help offload read operations from the primary database, improving performance during high traffic.

Use ElastiCache (Memcached): Adding an in-memory cache layer using Amazon ElastiCache reduces the load on the RDS instance by serving frequent queries directly from memory, especially when data is not updated often.

Option A (AWS WAF) is for web security, not database performance. Option D relates to storage optimization, not query latency. Option E would require re-architecting from relational to NoSQL, which is unnecessary and disruptive.

Option B: FSx for Lustre is designed for HPC workloads with high IOPS.

Option E: A cluster placement group ensures low-latency networking for HPC analytics workloads.

Option A: Amazon EFS is not optimized for HPC.

Option D: Mountpoint for S3 does not meet high IOPS needs.

Explanation (AWS Docs):

A: Use Direct Connect with a private VIF to ensure private connectivity from on-premises to AWS.

E: Peer the networking VPC with VPCs in the accounts hosting S3 buckets to allow private routing.

“A private virtual interface enables private connectivity to your VPC.”

— Direct Connect Private VIF

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Requirement Analysis: Need secure, direct connectivity from an on-premises client to an RDS cluster, accessible only when in the office.

VPC with Private Subnets: Ensures the RDS cluster is not publicly accessible, enhancing security.

Site-to-Site VPN: Provides secure, encrypted connection between on-premises office and AWS VPC.

Implementation:

Create a VPC with two private subnets.

Launch the RDS cluster in the private subnets.

Set up a Site-to-Site VPN connection with a customer gateway in the office.

Conclusion: This setup ensures secure and direct connectivity with minimal exposure, meeting the requirement for secure access from the office.

References

AWS Site-to-Site VPN:AWS Site-to-Site VPN Documentation

Amazon RDS:Amazon RDS Documentation

TheAWS WAF Bot Control managed ruleis designed to automatically detect and mitigate bot traffic. This feature is particularly useful for addressing malicious traffic and conserving compute resources by filtering unnecessary requests at the ALB level.

Option A:Blocking IP addresses manually introduces significant operational overhead and is not scalable against dynamic, worldwide malicious traffic.

Option C:AWS Shield provides DDoS protection, but the scenario does not describe a DDoS attack. WAF is better suited for managing application-layer threats like bot traffic.

Option D:The AWS WAF IP reputation rule helps block traffic from known bad IPs but may not address bot traffic effectively.

AWS Documentation References:

AWS WAF Bot Control

AWS WAF Managed Rules

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. By using Amazon ECS with the AWS Fargate launch type, you can run containers without managing servers or clusters. This combination reduces operational overhead and provides scalability.

To send data privately from on-premises to S3 buckets across multiple AWS accounts:

A: Using AWS Direct Connect with a private virtual interface (VIF) enables private connectivity from on-premises into the VPC.

E: After establishing the central VPC in a networking account, use VPC peering to allow access to S3 buckets across multiple AWS accounts.

“Direct Connect private virtual interface (VIF) allows private IP connectivity to VPC resources.”

“VPC Peering enables routing of traffic between VPCs using private IP addresses.”

— AWS Direct Connect Documentation

— VPC Peering Guide

Why not others:

B: Public VIF sends traffic over public AWS services—not fully private.

C: Interface endpoint is useful within a single account/VPC context.

D: Gateway endpoint doesn't solve inter-account routing.

Amazon RDS Multi-AZ deployments provide automatic failover for relational databases such as MySQL, ensuring high availability and durability. The feature maintains synchronous replication between a primary DB instance and a standby in a separate Availability Zone. AWS guarantees that failover typically completes within minutes, ensuring an RTO and RPO of less than 2 minutes. Option A requires manual promotion of replicas, which cannot meet the strict RTO/RPO requirement. Option C depends on custom scripts and manual synchronization, introducing operational risk. Option D creates active-active EC2-based databases, which do not provide synchronous replication or automated failover. Therefore, Multi-AZ RDS (B) is the managed, resilient, and operationally efficient solution that meets the business requirements.

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache for DynamoDB that delivers up to a 10x performance improvement—even at millions of requests per second. DAX requires minimal changes to existing applications and offloads the read workload from DynamoDB during report generation.

Reference Extract:

"DAX provides in-memory acceleration for DynamoDB tables, reducing response times from milliseconds to microseconds with minimal application changes."

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB and Performance section.

Auto Scalingis the most effective solution for ensuring seamless scalability of a stateless application. Key points:

Dleverages Auto Scaling with a Spot Fleet for cost efficiency and attaches an ALB to distribute traffic.

A and Bdo not provide automated scaling and would require manual intervention to add more instances.

Cchanges the instance type but does not scale out horizontally, which is required here.

AWS Documentation Reference:

Amazon EC2 Auto Scaling

The Application Load Balancer (ALB) supports sticky sessions (session affinity) using application cookies. AWS WAF integrates natively with ALB to provide Layer 7 protection at the same endpoint.

From AWS Documentation:

“You can enable sticky sessions for your Application Load Balancer target groups to ensure that a user’s requests are consistently routed to the same target. AWS WAF integrates with Application Load Balancer to protect your web applications from common exploits.”

(Source: Elastic Load Balancing User Guide & AWS WAF Developer Guide)

Why C and E are correct:

C: ALB operates at Layer 7 (HTTP/HTTPS), supports sticky sessions, and can serve as a public endpoint.

E: AWS WAF can be directly associated with the ALB to inspect traffic and enforce rules.Together, they fulfill both the security and session affinity requirements.

Why others are incorrect:

A: Network Load Balancer doesn’t support session affinity.

B: Gateway Load Balancer is used for virtual appliances, not web applications.

D: Using EIPs bypasses load balancing and WAF integration.

AWS Step Functions can orchestrate Lambda executions without modifying the Lambda code, and the Map state is designed to run a Lambda function once per item in a list—perfect for activating many devices in parallel or controlled batches.

This eliminates the loop inside the Retrieve function and prevents timeouts.

EventBridge (Option A) only changes the schedule. DynamoDB Streams (Option B) is unrelated to the activation workflow. EC2 (Option D) increases operational overhead.

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family's structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.

Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.

Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention. Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.

Amazon RDS for Oracle is a managed database service, which significantly reduces operational overhead compared to running Oracle on EC2 or on-premises. AWS Secrets Manager natively integrates with RDS and supports automatic, scheduled password rotation with minimal setup. You can configure the rotation schedule (including yearly), and Secrets Manager will handle the secure password storage and rotation workflow for you.

AWS Documentation Extract:

"AWS Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own infrastructure. You can configure automatic rotation for supported databases such as Amazon RDS for Oracle."

(Source: AWS Secrets Manager documentation)

A, C, D: These solutions require custom scripting, Lambda, and alarms, leading to more operational overhead.

To optimize storage costs, migrating data from Amazon EFS to Amazon S3 with the Intelligent-Tiering storage class is a cost-effective solution.

Amazon S3 Intelligent-Tiering: This storage class automatically moves data between frequent, infrequent, and archive access tiers based on access patterns, reducing storage costs without impacting performance.

Cost Savings: By leveraging Intelligent-Tiering, the company can achieve significant cost savings, especially for data with unpredictable access patterns.

Application Update: The application must be updated to interact with Amazon S3 APIs for storing and retrieving files, ensuring seamless integration with the new storage solution.

This approach provides a scalable, durable, and cost-effective storage solution, aligning with the company's optimization goals.

To securely integrate Amazon S3 with an application that uses Amazon Cognito for user authentication, the following two steps are essential:

Step 1: Create an Amazon Cognito Identity Pool (Option A)

Amazon Cognito Identity Poolsallow users to obtain temporary AWS credentials to access AWS resources, such as Amazon S3, after successfully authenticating with the Cognito user pool. The identity pool bridges the gap between user authentication and AWS service access by generating temporary credentials using AWS Identity and Access Management (IAM).

Once a user logs in using theCognito User Pool, the identity pool providesIAM roles with specific permissionsthat the application can use to access S3 securely. This ensures that each user has appropriate access controls while accessing the S3 bucket.

This is a secure way to ensure that users only have temporary and least-privilege access to the S3 bucket for their documents.

Step 2: Create an Amazon S3 VPC Endpoint (Option C)

By creating anAmazon S3 VPC endpoint, the company ensures that communication between the application (which is hosted in a private subnet) and the S3 bucket occurs over theAWS private network, without the need to traverse the internet. This enhances security and prevents exposure of data to public networks.

TheVPC endpointallows the application to access the S3 bucket privately and securely within the VPC. It also ensures that traffic stays within the AWS network, reducing attack surface and improving overall security.

Why the Other Options Are Incorrect:

Option B: This is incorrect becauseAmazon Cognito User Poolsare used for user authentication, not for generating S3 access tokens. To provide S3 access, you need to useAmazon Cognito Identity Pools, which offer AWS credentials.

Option D: ANAT gatewayis unnecessary in this scenario. Using aVPC endpointfor S3 access provides a more secure and cost-effective solution by keeping traffic within AWS.

Option E: Attaching a policy to restrict access based on IP addresses is not scalable or efficient. It would require managing users’ dynamic IP addresses, which is not an effective security measure for this use case.

AWS References:

Amazon Cognito Identity Pools

Amazon VPC Endpoints for S3

For logs that are:

Written and processed within a short period (24 hours)

Accessed quickly for compute/analytics

With unknown object count and size

Amazon S3 Standard is the most appropriate and cost-effective. Intelligent-Tiering is designed for data stored for longer periods (typically 30+ days) with changing access patterns and charges a per-object monitoring and automation fee that becomes inefficient for very short-lived objects.

S3 Glacier Deep Archive and S3 One Zone-IA are optimized for long-term archival or infrequently accessed data with retrieval time or availability constraints that are not suitable for nightly active processing.

Amazon Elastic File System (EFS) is a managed file storage service that can be mounted across multiple EC2 instances. It provides a scalable and high-performing solution to share data among instances within a VPC.

High Performance: EFS provides scalable performance for workloads that require high throughput and IOPS. It is particularly well-suited for applications that need to share data across multiple instances.

Ease of Use: EFS can be easily mounted on multiple instances across different Availability Zones, providing a shared file system accessible to all the instances within the VPC.

Security: EFS can be configured to ensure that data remains within the VPC, and it supports encryption at rest and in transit.

Why Not Other Options?:

Option A (Amazon S3 bucket with APIs): While S3 is excellent for object storage, it is not a file system and does not provide the low-latency access required for shared data between instances.

Option B (S3 bucket as a mounted volume): S3 is not designed to be mounted as a file system, and this approach would introduce unnecessary complexity and latency.

Option C (EBS volume shared across instances): EBS volumes cannot be attached to multiple instances simultaneously. It is not designed to be shared across instances like EFS.

AWS References:

Amazon EFS- Overview of Amazon EFS and its features.

Best Practices for Amazon EFS- Recommendations for using EFS with multiple instances.

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.

Amazon S3 Glacier Flexible Retrieval is a low-cost archival storage class that supports retrieval of data within minutes (expedited access ~1–5 minutes), making it ideal for audit scenarios where occasional, quick access to archived data is required. In contrast, Glacier Deep Archive takes hours to retrieve.

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.

SAML 2.0-based federation allows organizations to integrate their on-premises Active Directory with AWS, enabling Single Sign-On (SSO) for AWS Management Console and AWS CLI/API access. This lets users continue using their existing AD credentials, removing the need to manage separate identities for AWS and on-premises systems. Mapping roles to AD groups provides granular access control and seamless management.

Reference Extract from AWS Documentation / Study Guide:

"AWS supports SAML 2.0 for federated access, enabling integration with Active Directory or other identity providers to provide single sign-on to AWS resources without creating separate IAM users."

Source: AWS Certified Solutions Architect – Official Study Guide, Identity and Access Management section.

AWS recommends Auto Scaling with dynamic scaling policies to handle unpredictable workload spikes. Target tracking scaling policies automatically adjust capacity based on defined metrics such as average CPU utilization or request count per target. This approach ensures applications maintain responsiveness without overprovisioning. Scheduled scaling (options B and C) is only effective when traffic patterns are predictable, which is not the case here. Reserved Instances or Spot Instances also do not address sudden demand changes effectively. Replacing the ALB with an NLB (D) does not solve latency caused by EC2 instance capacity. Therefore, using EC2 instances in an Auto Scaling group with a target tracking scaling policy is the most effective and cost-efficient solution for unpredictable, short-lived traffic spikes.

AWS Global Accelerator reduces latency by directing users to the optimal Regional endpoint based on global network health and proximity. Amazon CloudFront caches static and dynamic content at edge locations for ultra-low latency access worldwide, improving performance and reducing server load.

A Network Load Balancer (NLB) supports IP address-based targets, which allows the use of both EC2 and on-premises endpoints. It also supports a static IP address or Elastic IP, which meets the requirement for a fixed IP address needed by some users.

NLB offers high performance, low latency, and minimal operational overhead. Application Load Balancer only supports instance or Lambda targets, not IP addresses outside the VPC. Route 53 Resolver is for DNS, and API Gateway is for HTTP-based APIs, not web applications.

AWS Control Tower provides a straightforward way to set up and govern a secure, multi-account AWS environment based on AWS best practices. It automates the setup of a baseline environment, or landing zone, that includes:

Service Control Policies (SCPs): These are used to manage permissions across AWS Organizations, allowing you to set permission guardrails. SCPs can restrict access to specific AWS services and actions, helping enforce security best practices.

Multi-Factor Authentication (MFA): AWS Control Tower can enforce MFA for the root user across all accounts, enhancing security.

Centralized Governance: It offers centralized logging and monitoring, making it easier to manage and audit multiple AWS accounts.

To prevent loss of access to the AWS root user account due to a lost MFA device, AWS recommends enabling multiple MFA devices for the root user.

Multiple MFA Devices: AWS allows up to eight MFA devices (virtual, hardware, or security keys) to be associated with a single root user account. This redundancy ensures that if one device is lost, others can be used to authenticate and access the account.

Security Best Practices: Implementing multiple MFA devices enhances account security and provides resilience against potential access issues.

By proactively setting up multiple MFA devices, the company can maintain uninterrupted access to its critical AWS resources, even in the event of a lost or malfunctioning MFA device.

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

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

VPC Endpoint for S3: A gateway endpoint for Amazon S3 enables you to privately connect your VPC to S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Configuration Steps:

In the VPC console, navigate to "Endpoints" and create a new endpoint.

Select the service name for S3 (com.amazonaws.region.s3).

Choose the VPC and the subnets where your EC2 instances are running.

Update the route tables for the selected subnets to include a route pointing to the endpoint.

Security Compliance: By configuring an S3 gateway endpoint, all traffic between the VPC and S3 stays within the AWS network, complying with the company's security regulations to avoid internet traversal.

Amazon EMR provides a managed big data framework that supports Apache Spark, which is ideal for distributed and compute-intensive data transformations. Managed scaling dynamically adjusts cluster resources, ensuring high performance with minimal management.

From AWS Documentation:

“Amazon EMR provides a managed environment for big data frameworks such as Apache Spark and Hadoop. With managed scaling, EMR automatically resizes clusters to meet workload demands.”

(Source: Amazon EMR Developer Guide)

Why B is correct:

Provides distributed parallel processing for large datasets.

Reduces operational overhead with managed scaling and auto-termination.

Integrates easily with S3, Glue, and ML pipelines.

Optimized for heavy ETL and analytics workloads.

Why others are incorrect:

A: Manual scaling and limited processing capacity.

C & D: Lambda has execution time and memory limits unsuitable for 30-minute compute-intensive tasks.

Amazon S3 File Gateway provides a local NFS mount point, which can be used with minimal changes by the legacy application. Files are transparently uploaded to Amazon S3, allowing the web application to access them directly from S3. This is the most cost-effective and operationally simple way to bridge legacy on-premises NFS output with S3, requiring no changes to the batch process.

AWS Documentation Extract:

“With S3 File Gateway, you can provide applications a local file interface to Amazon S3. S3 File Gateway presents a file-based interface (NFS or SMB), allowing you to use S3 as your scalable, durable storage while making files available to legacy applications.”

(Source: AWS Storage Gateway documentation)

A: Outposts is far more costly and complex than needed.

B: Volume Gateway presents iSCSI block storage, not NFS.

C: Requires re-coding the legacy app to use S3 APIs, not NFS.

Big data workloads that need to process terabytes of data in memory requirememory-optimized instancesfor the core and task nodes to ensure sufficient memory for processing data efficiently.

Primary Node:Ageneral purpose instanceis suitable because it manages cluster operations, including coordination and monitoring, and does not process data directly.

Core and Task Nodes:These nodes handle data storage and processing.Memory-optimized instancesare ideal because they provide high memory-to-CPU ratios, which is critical for in-memory big data workloads.

Why Other Options Are Incorrect:

Option A:Storage optimized and compute optimized instances are not suitable for workloads that rely heavily on in-memory processing.

Option B:A memory-optimized primary node is unnecessary because the primary node does not process data.

Option D:General purpose instances for all nodes will not provide sufficient memory for processing terabytes of data in memory.

AWS Documentation References:

Amazon EMR Instance Types

Memory-Optimized Instances

Option Cis the most secure and practical solution. Presigned URLs allow temporary, limited access to upload files to specific S3 prefixes. This ensures that artists can upload files securely without needing AWS credentials or accounts. Each artist receives a unique URL with permissions tied to the intended S3 location, and the URL can be configured to expire after a certain time, minimizing security risks.

Why Other Options Are Incorrect:

Option A:Enabling CORS allows cross-origin access but does not provide authentication or authorization for uploads. This does not secure the uploads or restrict access to specific artists.

Option B:Turning off block public access and sharing the bucket URL exposes the bucket to potential misuse and unauthorized uploads. This approach is highly insecure.

Option D:While a web interface might work, it introduces additional complexity and potential security risks by exposing upload functionality through a public-facing application.

AWS Documentation References:

Using Amazon S3 Presigned URLs

AWS Best Practices for Secure S3 Access

S3 Object Lock in Compliance Mode prevents objects from being deleted or overwritten for a fixed, specified retention period. Compliance Mode is specifically designed to meet regulatory requirements, ensuring that no user, including the root user, can modify or delete the object during the retention period.

Reference Extract:

"S3 Object Lock in compliance mode protects objects from being deleted or overwritten during the specified retention period, helping meet regulatory retention requirements."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Object Lock and Compliance section.

AWS Global Accelerator is a service that improves global application availability and performance using the AWS global network. It supports both TCP and UDP protocols and provides optimized routing and lower latency for real-time applications such as VoIP, regardless of where the user is located globally.

AWS Documentation Extract:

"AWS Global Accelerator uses the AWS global network to optimize the path to your application endpoints, improving performance for TCP and UDP traffic, such as VoIP."

(Source: AWS Global Accelerator documentation)

B: CloudFront accelerates HTTP/S content, not TCP/UDP.

C: Route 53 geoproximity routing is for DNS-based routing, not for traffic acceleration.

D: Network Load Balancers support TCP/UDP, but do not address global latency or provide acceleration.

AmazonEventBridgeAPI destinations allow you to send data from AWS to external systems, like Salesforce, using HTTP APIs, including those secured with OAuth. This provides a secure and scalable solution for sending messages from the order processing application to Salesforce.

Option A and B (SNS): SNS is not ideal for OAuth-secured external APIs and lacks the necessary OAuth integration.

Option D (MSK): Amazon MSK is a Kafka-based streaming solution, which is overkill for simple message forwarding to Salesforce.

AWS References:

Amazon EventBridge API Destinations

The best solution is to useAmazon SQSto receive updates from the mobile app and process them with anAWS Lambdafunction. The Lambda function can rewrite the message body as necessary for each shipper and then publish the updates to the appropriateSNS topicfor distribution. Thissetup 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

AWS KMS with SSE-KMS provides granular key management and auditability. All use of KMS keys is logged in AWS CloudTrail, which allows compliance teams to monitor encryption and decryption operations. A bucket policy can be configured to enforce uploads only with the designated KMS key, ensuring that users cannot bypass encryption or change methods. Option A (SSE-S3 with bucket policy) enforces encryption but does not provide the same level of control or auditable key usage. Option C (client-side encryption) increases complexity and key management burden. Option D prevents bucket setting changes but does not prevent unencrypted uploads. Therefore, B ensures the most granular control, auditability, and compliance with financial data requirements.

The company wants to move from an on-premises Docker environment to fully managed AWS services with persistent storage. The best fit is:

Amazon ECS with AWS Fargate launch type: This is a serverless container orchestration solution where AWS manages the underlying infrastructure, removing the need to manage EC2 or Kubernetes nodes.

Amazon EFS (Elastic File System): This is a fully managed, scalable, and shared file system for use with ECS tasks. It supports persistent storage for containers, replacing the local volumes used on-premises.

This combination (ECS + Fargate + EFS) is fully managed and requires no manual server maintenance.

Option A uses EKS with self-managed nodes, which is not fully managed.

Option C (DynamoDB) is for structured key-value storage, not for persistent file storage.

Option D uses ECS with EC2 launch type, which is not serverless and requires managing instances.

???? Reference:

Using Amazon ECS with AWS Fargate

Mounting EFS volumes in ECS tasks

Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.

This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.

The most resilient and scalable architecture for handling millions of high-resolution images with frequent updates is to store the binary images in Amazon S3 and store their metadata or reference (geographic code and S3 URL) in Amazon DynamoDB.

From AWS Documentation:

“Store large objects like images in Amazon S3 and use Amazon DynamoDB to store metadata and references. This design pattern is scalable, highly available, and cost-effective.”

(Source: AWS Architecture Blog – Best Practices for Handling Large Objects)

Why B is correct:

Amazon S3 is designed for storing large volumes of binary data (images).

DynamoDB provides low-latency reads/writes using the geographic code as the partition key.

Highly available, serverless, and auto-scaling, suitable for disaster scenarios with bursts of activity.

Reduces pressure on the database layer by separating metadata from image storage.

Why the others are incorrect:

Option A and D: Storing images directly in RDS is expensive, unscalable, and not optimal for binary storage.

Option C: DynamoDB is suitable, but storing actual binary image data in DynamoDB is not best practice due to item size limits (400 KB) and performance concerns.

To improve availability and fault tolerance of an Amazon RDS instance, the recommended approach is to configure a Multi-AZ deployment.

Multi-AZ deployments for RDS automatically replicate data to a standby instance in a different Availability Zone (AZ).

If a failure occurs in the primary AZ (due to hardware, network, or power), RDS will automatically failover to the standby instance with minimal downtime, without administrative intervention.

This is an AWS-managed feature and does not require application modification.

It does not provide scalability or load balancing; it's designed for high availability and resiliency.

Options A, B, and D are incorrect:

A refers to cross-Region, which is used for disaster recovery, not high availability.

B and D with SQS do not address high availability directly for the RDS instance; queues help decouple systems but do not make a database more resilient.

???? Reference:

Amazon RDS Multi-AZ Deployments

For bursty and seasonal workloads, AWS recommends serverless architectures using Amazon API Gateway + AWS Lambda:

API Gateway exposes a fully managed, scalable API endpoint.

Lambda provides automatic scaling based on request volume, with no need to provision or manage servers.

This is ideal for workloads with variable or seasonal demand, such as end-of-year tax computation bursts.

This combination minimizes operational overhead (no OS patching, no capacity planning) while scaling seamlessly to meet peak demand.

Options A, C, and D rely on EC2 instances. They require capacity planning, scaling configuration, and ongoing management. They do not scale as effortlessly as Lambda for unpredictable or highly seasonal workloads.

Predictive scaling in EC2 Auto Scaling uses machine learning to analyze historical load and forecast future capacity needs, then pre-scales capacity before the demand occurs. It is specifically designed for recurring, cyclical workloads such as weekly batch jobs.

In this scenario:

The workload pattern is known and recurring (weekly scripted jobs).

The company explicitly does not want to manually analyze trends.

They need capacity to be ready 30 minutes before jobs start.

Predictive scaling lets you:

Set the metric (CPU utilization) and target (60%).

Configure the policy to pre-launch instances 30 minutes in advance, satisfying the requirement automatically with minimal operational overhead.

Dynamic scaling (Option A) reacts after CPU increases, not before.

Scheduled scaling (Option B) requires manual analysis and ongoing adjustment of capacity values.

EventBridge + Lambda (Option D) introduces unnecessary custom logic and still reacts after CPU reaches 60%, not 30 minutes before.

Gateway Endpoint for Amazon S3: A VPC endpoint for Amazon S3 allows you to privately connect your VPC to Amazon S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select "Endpoints" and create a new endpoint.

Choose the service name for S3 (com.amazonaws.region.s3).

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

AWS Lake Formationis designed for managing fine-grained access control to data in an efficient manner:

Granular Permissions: Lake Formation allows column-level, row-level, and table-level access controls, which can precisely define access to PII data.

Integration with AWS Glue Catalog: Lake Formation natively integrates with AWS Glue for seamless data cataloging and access control.

Operational Efficiency: Centralized access control policies minimize the need for separate IAM roles or policies.

Why Other Options Are Not Ideal:

Option A:

Creating multiple IAM policies introduces complexity and lacks column-level access control.Not efficient.

Option B:

Managing multiple IAM roles for granular access is operationally complex.Not efficient.

Option D:

Creating views in Glue adds unnecessary complexity and may not provide the level of granularity that Lake Formation offers.Not the best choice.

AWS References:

AWS Lake Formation:AWS Documentation - Lake Formation

Fine-Grained Permissions with Lake Formation:AWS Documentation - Fine-Grained Permissions

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

Amazon Elastic File System (Amazon EFS) is a fully managed, elastic, shared file system that can be mounted concurrently by multiple EC2 instances across multiple Availability Zones. EFS automatically grows and shrinks as files are added or removed, providing seamless scalability for unpredictable and growing storage needs. It supports simultaneous access from multiple compute resources, making it ideal for applications where several EC2 instances must read and write to the same data set concurrently. EBS volumes cannot be shared across multiple Availability Zones, and S3 Glacier is intended for archival and infrequent access, not for active, hourly data analysis.

Reference Extract from AWS Documentation / Study Guide:

"Amazon EFS provides a scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources. It can be mounted to many EC2 instances across multiple Availability Zones and is ideal for shared data scenarios."

Source: AWS Certified Solutions Architect – Official Study Guide, Storage Solutions section; Amazon EFS User Guide.

A: Amazon Macie can scan S3 buckets for sensitive data and identify PII.

C: S3 Object Lock legal hold prevents object deletion until a review is complete, meeting compliance requirements.

E: EventBridge can trigger the Lambda function automatically when Macie detects sensitive data, creating an automated workflow.

AWS Documentation Extract:

"Amazon Macie automatically discovers, classifies, and protects sensitive data in AWS. You can use EventBridge to invoke a Lambda function for post-processing, such as applying Object Lock legal hold to flagged objects."

(Source: AWS Macie and S3 Object Lock documentation)

B, D: Moving to Glacier Deep Archive or applying retention in governance mode is not specific to deletion prevention pending review.

F: MFA Delete adds a security layer but does not automate object retention for flagged PII.

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notificationscan be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouplesthe video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use ofAuto Scalingensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notificationsdetails how to configure notifications for S3 events.

Amazon SQSis a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Edge-Optimized API: Designed for global users by routing requests through CloudFront's edge locations, reducing latency.

Content Encoding: Enabling content encoding compresses data, further optimizing performance by decreasing payload size.

Caching: Adding API Gateway caching reduces the number of calls to Lambda and database backends, improving latency.

Reserved Concurrency: Although useful, this does not directly affect latency for transferring static and dynamic content.

AWS API Gateway Edge-Optimized APIs Documentation

To enable DNS resolution from AWS VPCs to on-premises DNS servers over Direct Connect or VPN, AWS recommends using Amazon Route 53 Resolver with outbound endpoints. An outbound endpoint allows DNS queries originating in the VPC to be forwarded to a customer-managed DNS server (e.g., on-prem).

Next, a forwarding rule (Forward type) must be created to forward DNS queries for the custom domain internal.example.com to the on-premises DNS IP addresses. This rule defines what domain names are forwarded and to which DNS servers.

Finally, the rule must be associated with all workload VPCs to allow those VPCs to use the rule. There is no need to deploy endpoints in every VPC — one outbound endpoint is sufficient and can be shared across VPCs via rule association.

???? Reference:

Route 53 Resolver Endpoints

Best practices for hybrid DNS resolution

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

Amazon RDS Blue/Green Deployments provide a safe and straightforward way to make database changes with minimal downtime and risk. Blue/Green deployments create an exact copy ("green") of your production environment ("blue") where you can make schema changes and run tests. After validation, you can promote the green environment to production with a single click or API call, achieving near-zero downtime and maximum availability. This is the AWS-recommended method for deploying major database changes in a way that minimizes impact to users and maximizes uptime.

Reference Extract from AWS Documentation / Study Guide:

"Amazon RDS Blue/Green Deployments enable you to make changes to your database environment safely. You can perform schema updates and feature testing in a fully managed staging environment and switch over with minimal downtime, ensuring the highest availability."

Source: AWS Certified Solutions Architect – Official Study Guide, Database and Migration section; Amazon RDS Blue/Green Deployments Documentation.

Since the application uses long-lived TCP connections and must remain idle for up to 1 hour without timeout, all components involved in the connection path (ALB, GWLB, and firewall) must have their idle timeout values configured to at least 1 hour.

Gateway Load Balancer supports transparent insertion of firewalls, and configuring consistent idle timeouts ensures connections don’t drop mid-session.

Using just one firewall (option B) introduces a single point of failure. Increasing capacity (option D) doesn't solve idle timeout issues. Therefore, C provides a resilient and complete configuration.

Amazon FSx for ONTAP supports both NFS and SMB protocols and includes automated tiering between SSD and capacity pool storage, optimizing cost and performance. It is ideal for mixed operating systems and varied access patterns with minimal administrative overhead.

AWS best practice for a highly available, resilient web application is:

Run EC2 instances in an Auto Scaling group across multiple Availability Zones.

Put an Application Load Balancer (ALB) in front of the Auto Scaling group.

Use CloudWatch alarms and scaling policies for horizontal scaling based on demand.

Option D implements exactly this: a minimum of three instances spread across AZs (resilience to AZ failure), behind an ALB, with automatic horizontal scaling. This provides both elasticity and high availability with good cost efficiency (instances scale out/in based on traffic).

Option A uses only three fixed instances and relies on vertical scaling, which does not scale as well and is less resilient.

Option B overprovisions to nine instances from the start, which is unnecessarily expensive.

Option C keeps all instances in a single AZ, which does not meet resilience requirements.

AWSService Catalogis designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn't provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

Amazon QLDB is the most cost-effective and suitable service for maintainingimmutable, cryptographically verifiable logsof data changes. QLDB provides a fully managed ledgerdatabase with a built-in cryptographic hash chain, making it ideal for recording changes to accounting records, ensuring data integrity and security.

QLDB reduces operational overhead by offering fully managed services, so there’s no need for server management, and it’s built specifically to ensure immutability and verifiability, making it the best fit for the given requirements.

Option A (Redshift): Redshift is designed for analytics and not for immutable, cryptographically verifiable logs.

Option B (Neptune): Neptune is a graph database, which is not suitable for this use case.

Option C (Timestream): Timestream is a time series database optimized for time-stamped data, but it does not provide immutable or cryptographically verifiable logs.

AWS References:

Amazon QLDB

How QLDB Works

AWS Fargate runs containers without managing servers, and ECS Service Auto Scaling adjusts the number of tasks based on demand. Behind an Application Load Balancer, ECS services elastically scale out to handle concurrent long-running requests. This fits workloads with processing times of 5–20 minutes per request. Lambda (C, D) has a maximum 15-minute timeout, so a portion of requests would fail or require complex refactoring and orchestration. Option B relies on vertical scaling and manual intervention, leaving capacity idle and failing to scale automatically with spikes. By containerizing the app and running on ECS with Fargate, the company gains automatic scaling, isolation per task, and no instance management, ensuring reliable throughput as request volume grows while keeping operations minimal.

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

EBS gp3 volumes allow you to independently configure IOPS and throughput, up to 16,000 IOPS, regardless of the volume size, and at a lower cost compared to io1/io2 provisioned IOPS volumes. This meets the requirement for cost-effective, predictable IOPS performance for Amazon RDS for PostgreSQL.

AWS Documentation Extract:

"With gp3 volumes, you can provision performance independent of storage capacity, up to 16,000 IOPS. This allows for cost-effective scaling for applications that require high performance at a lower price point compared to io1."

(Source: Amazon EBS documentation, gp3 Volumes)

A: gp2 ties IOPS to volume size; 5 TiB is wasteful if only 15,000 IOPS are needed.

B: io1 works but is significantly more expensive than gp3 for most workloads.

D: Magnetic volumes do not support high IOPS.

The most cost-effective way to provide consistent SQL query performance on a heavily structured dataset, where some data is accessed more frequently, is to use Amazon Redshift as your main data warehouse for hot data and Amazon Redshift Spectrum to query cold data that remains in S3. With this approach, frequently queried data is loaded into Redshift for fast, consistent querying, while infrequently accessed data is left in S3 and accessed on demand via Spectrum, avoiding unnecessary data warehousing costs. Amazon Kinesis Data Firehose provides an easy and scalable way to ingest streaming data directly to both S3 and Redshift.

AWS Documentation Extract:

"Amazon Redshift Spectrum allows you to run queries against exabytes of data in Amazon S3 without loading or transforming the data. Load hot data into Redshift for fast access and query cold data in S3 with Spectrum, optimizing both cost and performance."

(Source: Amazon Redshift documentation, Spectrum)

A: Athena is good for ad hoc queries but not for consistent, high-performance SQL workloads.

B, C: Loading all data into Redshift is not cost-effective if some data is infrequently accessed.

Comprehensive and Detailed Explanation (from AWS Solutions Architect documentation):

Amazon DynamoDB is a fully managed NoSQL database engineered for extremely high throughput and millisecond-level response times at any scale. It can automatically scale to support millions of requests per second and requires no management of infrastructure, storage provisioning, or server maintenance.

AWS documentation specifically highlights DynamoDB as the optimal solution for ecommerce workloads that require consistent low-latency performance, massive scalability, and near-zero operational overhead.

Aurora and RDS cannot support millions of requests per second, and Redshift is a data warehouse not intended for transactional workloads.

Setting the RDS backup retention policy to 30 days forautomated backupsthrough AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are notautomatically managed by RDS's retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.

Key AWS features:

Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.

Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.

AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups​.

According to AWS best practices, each tier’s security group must restrict inbound traffic to only the upstream trusted source. For the web tier, the Application Load Balancer must be the only entity allowed to send traffic. AWS documentation specifies: “Restrict the security groups associated with your targets to accept traffic only from the load balancer.” This confirms that the web tier security group should allow inbound HTTPS from the ALB security group (A).

For communication between the web and application tiers, AWS states: “You can specify a security group as the source or destination in a rule” and “Create rules only for the protocols and ports required by your application.” Therefore, the application tier security group must allow inbound HTTPS traffic from the web tier security group (E).

For the database tier, AWS guidance says: “Allow only the necessary ports for database communication.” Microsoft SQL Server listens on port 1433 by default, so the database tier security group must allow inbound SQL Server traffic from the application tier security group (C).

Outbound rules (options B, D, and F) are unnecessary because AWS specifies that “Security groups are stateful. Return traffic is automatically allowed.” This means once inbound rules are defined, the return path is automatically permitted without extra outbound configurations.

This combination (A, C, E) applies the principle of least privilege, ensures end-to-end secure communication across tiers, and follows AWS recommendations for ALB-to-target security group setups.

AWS recommends using Amazon ElastiCache as an in-memory caching layer in front of relational databases such as Amazon RDS to offload read traffic and significantly improve performance for read-heavy workloads. ElastiCache (Redis OSS) provides microsecond latency and can cache the results of frequent or expensive queries without requiring any change to the underlying database schema or engine. This directly addresses the requirement to improve performance without changing the database structure and with minimal operational overhead, because ElastiCache is a fully managed service (patching, failure detection, replacement, etc., are handled by AWS).

Option A (DynamoDB) would require a full data migration and application changes, including schema and query rewrites.

Option C (Memcached on EC2) introduces additional management overhead for EC2 instances (scaling, patching, HA).

Option D (DAX) is a caching layer only for DynamoDB and cannot be used directly with Amazon RDS.

To ensure that log files are immutable and cannot be deleted or overwritten for a specified retention period, Amazon S3 offers Object Lock in Compliance Mode. When enabled:

Compliance Mode: Ensures that a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account, for the duration of the retention period. AWS Documentation

S3 Versioning: Must be enabled on the bucket to use Object Lock. This allows multiple versions of an object to be stored, ensuring that previous versions are preserved and protected. Amazon Web Services, Inc.

By enabling S3 Versioning and applying Object Lock in Compliance Mode with a 1-year retention period, the company can meet regulatory requirements to retain log data securely and prevent any modifications or deletions during that time.

Deploying the web application on EC2 instances in private subnets behind an internal ALB ensures that the application is not directly accessible from the internet. By setting up a Site-to-Site VPN connection, the application can be accessed securely from the company's office network. Deploying NAT gateways in public subnets allows instances in private subnets to initiate outbound connections to the internet for downloading security patches.

For processing thousands of images and generating large files while minimizing manual tasks and operational overhead, usingAWS Batchis the best solution. AWS Batch allows you to run large-scale, parallel, and managed batch computing jobs without needing to manage the underlying infrastructure.

AWS Batch: Automates the image processing jobs, dynamically allocating the necessary resources based on the job requirements, which reduces operational overhead.

AWS Step Functions: Orchestrates the entire image processing workflow, ensuring that tasks are executed in the correct sequence, improving manageability.

Amazon S3: Stores the processed files, providing scalable and cost-effective storage.

Option A (ECS with EC2 Spot Instances): While cost-effective, managing ECS and Spot Instances involves more operational effort.

Option C (Lambda with EC2 Spot): Lambda functions have size and duration limitations, making them less suited for large image processing tasks.

Option D (EC2 with Step Functions): Managing EC2 instances involves more overhead than using AWS Batch.

AWS References:

AWS Batch

AWS Step Functions

Amazon QuickSight includes built-in ML-powered forecasting capabilities, allowing you to create, visualize, and interact with time series forecasts directly within the BI dashboard, with no ML experience or infrastructure management required. This is the lowest management overhead solution.

AWS Documentation Extract:

"Amazon QuickSight provides built-in ML-powered forecasting, allowing business users to forecast future trends with a few clicks and no machine learning expertise required."

(Source: Amazon QuickSight documentation)

A, C, D: Require additional setup, management, or ML knowledge.

AWS Snowball Edge is specifically designed for use cases that involve limited or unreliable network connectivity. It enables data transfer and local compute processing at edge locations.

It comes in two options: Snowball Edge Storage Optimized and Snowball Edge Compute Optimized.

The Compute Optimized model allows the disaster response team to both store images locally and process data on the device using Amazon EC2-compatible compute resources.

This removes the need for constant network connectivity. After processing, the device can be shipped back to AWS, where data is uploaded to S3.

Other options fail due to:

SQS not being suitable for large binary image data (Option B)

Kinesis Data Firehose needing steady connectivity (Option C)

Storage Gateway is for hybrid cloud environments with ongoing connection, not rugged field use (Option D)

???? References:

AWS Snowball Edge Overview

Snowball Edge Use Cases

AWS Secrets Manager natively supports automatic rotation of RDS for Oracle credentials, fully integrating with Amazon RDS. Rotation workflows are managed automatically by the service, eliminating custom scripting and reducing operational effort.

Migrating to EC2 (Option C) requires custom rotation logic. Converting to DynamoDB or Neptune (Options A and D) would require complete database redesign and do not fulfill the requirement to run Oracle.

To securely migrate an on-premises Oracle database to Amazon Aurora, the following steps are recommended:

Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS): AWS SCT helps convert the source database schema to a format compatible with the target database (Aurora). AWS DMS facilitates the actual data migration, ensuring minimal downtime and data integrity.

Use AWS Site-to-Site VPN: Establishing a Site-to-Site VPN connection provides a secure and encrypted tunnel between the on-premises environment and AWS. This ensures that data transferred during the migration is protected against interception and unauthorized access.

Amazon FSx for Lustre: Lustre is a high-performance file system designed for workloads that require fast storage with sustained high throughput and low latency. It integrates with Amazon S3, making it suitable for HPC environments.

Persistent File Systems:

Persistent Storage: Suitable for long-term storage and recurrent use, providing durability and availability.

High Throughput and Low Latency: Persistent Lustre file systems can handle large amounts of data with sub-millisecond latency, meeting the needs of high-performance computing workloads.

Simultaneous Access: FSx for Lustre allows thousands of compute instances to access and process large datasets concurrently, ensuring that the high volume of data is handled efficiently.

Highly Available: FSx for Lustre is designed to provide high availability and is managed by AWS, reducing the operational burden.

When AWS workloads must resolve DNS names from on-premises systems over a hybrid network (like VPN or Direct Connect), the best solution is to use Amazon Route 53 Resolver outbound endpoints.

The outbound endpoint enables DNS queries to be forwarded from your VPC to on-premises DNS servers.

You must also configure a Route 53 Resolver forwarding rule to define which domain names (e.g., corp.internal) should be forwarded to the specific on-premises DNS IPs.

This setup allows private DNS resolution from AWS to on-premises systems and is fully managed, eliminating the need to run and maintain EC2-based DNS proxies (as in option A).

Options C and D are incorrect:

C is not DNS-specific and doesn’t solve name resolution.

D misuses a public hosted zone for a private DNS domain.

???? Reference:

Route 53 Resolver Outbound Endpoints

A. Fixed desired instances:Does not adapt to traffic load fluctuations, leading to inefficiencies.

B. Larger EC2 instances:Increases costs unnecessarily.

C. Target tracking scaling policy:Adjusts capacity based on actual demand, optimizing costs.

D. Instance store volumes:Not persistent and unsuitable for shared data across instances.

Using a Lambda function as part of the Kinesis Data Firehose pipeline allows for real-time detection and masking of PII before data is written to S3. This ensures that PII is never stored in its raw form in the data lake.

Option B:Amazon Macie can scan and classify data but does not provide in-line PII masking for data ingestion.

Option C:Server-side encryption secures data but does not mask PII.

Option D:CloudHSM is unnecessary for PII masking and adds complexity without addressing the requirements.

AWS Documentation References:

Using Lambda with Kinesis Data Firehose

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

The Amazon CloudWatch agent is required to collect memory utilization metrics (as memory metrics are not reported by default). A target tracking policy is the simplest and most effective way to scale based on a custom metric such as mem_used_percent.

Reference Extract:

"Install the CloudWatch agent to collect memory metrics, and create a target tracking scaling policy using these custom metrics."

Source: AWS Certified Solutions Architect – Official Study Guide, Monitoring and Scaling section.

Amazon EMR on EC2 supports “runtime roles (application-scoped IAM roles)” so each application/step assumes its own IAM role with least-privilege S3 access. You enable this via an EMR security configuration by setting “EnableApplicationScopedIAMRole = true.” The EMR core/Task nodes run under the cluster’s EC2 instance profile; therefore the instance profile must be permitted to “sts:AssumeRole” into the defined EMR runtime roles, and each runtime role must trust the instance profile (trust policy principal is the instance profile role). This design limits each job’s S3 scope via role policies and enforces per-job access segregation. Option B reverses the trust (incorrect). Option E trusts the EMR service role (not used to assume runtime roles). Option F is unrelated (encryption in transit). The correct trio is to enable application-scoped roles (A), authorize the instance profile to assume them (C), and configure the runtime roles’ trust relationship to allow that assumption (D).

Versioning in S3 preserves every version of every object — preventing permanent overwrites. This ensures compliance where data cannot be overwritten or lost.

SSE-S3 ensures server-side encryption.

“When you enable versioning, Amazon S3 stores every version of every object. With versioning, you can preserve, retrieve, and restore every version of every object stored in an S3 bucket.”

— S3 Versioning

This satisfies regulatory requirements for protecting data from overwriting indefinitely.

Incorrect Options:

B: Object Lock with retention period is time-bound, not indefinite.

C: Legal hold blocks deletion but doesn’t directly prevent overwriting.

D: Storage Lens is analytics, not protection.

To decouple the monolith and enhance scalability, AWS best practice is to introduce an asynchronous message queue, such as Amazon SQS, between the web/API tier and the order-processing logic.

AWS Lambda functions consuming from the SQS queue provide serverless, auto-scaling processing without managing servers.

To track and reprocess failed orders, SQS supports dead-letter queues (DLQs). Messages that cannot be processed successfully after a configurable number of attempts are automatically moved to the DLQ, where operations teams or automated processes can inspect and reprocess them.

Why others are not correct:

B: ECS tasks can consume an SQS queue, but this requires managing container infrastructure and does not inherently provide as simple reprocessing/visibility as combining Lambda with a DLQ. Visibility timeout is not a tracking or archival mechanism.

C: Kinesis is a streaming service designed for ordered event streams, not primarily for order-queue semantics and DLQs; SQS is simpler and purpose-built for this pattern.

D: Long polling reduces empty responses and API calls but does nothing for tracking or reprocessing failed messages; without a DLQ, failed orders are harder to manage

Using API Gateway with API keys provides secure access control. Amazon SQS allows asynchronous decoupling between frontend and backend, ensuring that backend processing can scale independently. AWS Lambda reading from SQS ensures scalable, event-driven processing with minimal operational management. This architecture is resilient and decoupled.

AWS PrivateLinkis the most suitable solution for providing fine-grained access control while allowing multiple VPCs, potentially across multiple accounts, to access the new application. This approach offers the following advantages:

Fine-grained control: Endpoint policies can restrict access to specific services or principals.

No need for route table updates: Unlike VPC peering or transit gateways, AWS PrivateLink does not require complex route table management.

Scalable architecture: PrivateLink scales to support traffic from multiple VPCs.

Secure connectivity: Ensures private connectivity over the AWS network, without exposing resources to the internet.

Why Other Options Are Not Ideal:

Option A:

VPC peering is not scalable when connecting multiple VPCs or accounts.

Route table management becomes complex as the number of VPCs increases.Not scalable.

Option B:

While transit gateways provide scalable VPC connectivity, they are not ideal for fine-grained access control.

Transit gateways allow connectivity but do not inherently restrict access to specific applications.Not ideal for fine-grained access control.

Option D:

Exposing the application through an ALB over the internet is not secure and does not align with the requirement to use private network resources.Security risk.

AWS References:

AWS PrivateLink:AWS Documentation - PrivateLink

AWS Networking Services Comparison:AWS Whitepaper - Networking Services

The Requester Pays feature in Amazon S3 allows the bucket owner to configure the bucket so that the requester, rather than the bucket owner, pays for data transfer and request costs. This is ideal for sharing large datasets with the public or with other AWS accounts when you want to minimize your own data transfer expenses.

Reference Extract from AWS Documentation / Study Guide:

"Requester Pays buckets allow you to configure the bucket so that the requester instead of the bucket owner pays the cost of the request and the data download from the bucket."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Cost Management section.

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

AWS Transit Gateway simplifies network connectivity by acting as a hub that can connect VPCs and on-premises networks through VPN or Direct Connect. It provides scalability and reduces administrative overhead by eliminating the need to manage complex peering relationships as the number of accounts and VPCs grows.

To make the application highly available across regions:

Deploy the application in a different region using a newECS clusterandALBto ensure regional redundancy.

UseRoute 53 failover routingto automatically direct traffic to the healthy region in case of failure.

UseDynamoDB Global Tablesto ensure the database is replicated and available across multiple regions, supporting read and write operations in each region.

Option D (EKS cluster in the same region): This does not provide regional redundancy.

Option E (Global Secondary Indexes): GSIs improve query performance but do not provide multi-region availability.

Option F (PrivateLink): PrivateLink is for secure communication, not for cross-region high availability.

AWS References:

DynamoDB Global Tables

Amazon ECS with ALB

Amazon Route 53 supports failover routing policies, which use health checks to route DNS queries to a secondary Region only if the primary endpoint fails. This design ensures only one Region is active for traffic at any given time. This is the recommended architecture for active-passive, multi-Region DR strategies.

AWS Documentation Extract:

“Failover routing lets you route traffic to a primary resource, such as a web server in one Region, and a secondary resource in another Region. If the primary fails, Route 53 can route traffic to the secondary resource automatically.”

(Source: Amazon Route 53 documentation, Routing Policy Types)

A, D: These options do not configure DNS failover for external users.

C: Geolocation routing is for regional distribution, not DR failover.

A & B. GuardDuty:Designed for threat detection, not for identifying or classifying sensitive data in S3 buckets.

C. Macie with EventBridge + SNS:Automatically identifies sensitive data, triggers alerts, and uses SNS for immediate notification via email.

D. Macie with EventBridge + SQS:Introduces latency due to periodic polling and adds unnecessary complexity.

For infrequent or ad hoc querying of log data, Amazon S3 + Amazon Athena provides the most cost-effective, serverless, and scalable analytics solution.

From AWS Documentation:

“Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.”

(Source: Amazon Athena User Guide)

Why A is correct:

Amazon S3 offers durable, scalable, and cost-efficient storage.

Athena allows SQL-based querying on structured or semi-structured data like logs.

No need to provision or manage infrastructure.

Ideal for occasional querying at low cost.

Why the others are not optimal:

Option B: OpenSearch adds cost and is best for frequent, low-latency log querying.

Option C: RDS is not optimized for large-scale write-heavy log ingestion and costs more.

Option D: CloudWatch Logs is suitable for real-time monitoring, not for long-term storage and analytics of large log volumes.

AWS Backup is a fully managed backup service designed to centralize and automate data protection across AWS services including EC2 and RDS. It allows users to define backup schedules (backup plans) and automatically create and retain backups. AWS Backup also offers restore testing plans, allowing users to automate the validation of backups by restoring them in a controlled manner. This service is built to minimize operational overhead by removing the need to manage custom scripts, manual processes, or additional orchestration services. This aligns with AWS best practices for resilience, automation, and operational excellence.

Reference Extract from AWS Documentation / Study Guide:

"AWS Backup enables you to centralize and automate data protection across AWS services. You can create backup plans, schedule backups, and set lifecycle policies. AWS Backup also enables restore testing to verify your backup integrity, with minimal manual intervention."

Source: AWS Certified Solutions Architect – Official Study Guide, Resiliency and Disaster Recovery section; AWS Backup User Guide.

AWS Well-Architected recommends multi-AZ, elastic architectures: use Auto Scaling groups across multiple Availability Zones for EC2 to eliminate single-AZ failure and automatically replace capacity. For relational databases, Amazon RDS Multi-AZ provides synchronous replication and automatic failover for high availability. Serving static content via Amazon CloudFront (with S3 origin) improves resiliency and performance by caching at edge locations and reducing origin load/latency. Options A and C concentrate compute in one AZ and lack resilient static delivery. Option D changes the stack unnecessarily and proposes EFS One Zone-IA for static assets, which is not multi-AZ and is intended for infrequently accessed, single-AZ workloads, reducing resilience. Therefore, B aligns with best practices for high availability, fault tolerance, and global performance.

Amazon S3 Standard provides virtually unlimited scalability, high durability (11 nines), and millisecond latency. It is designed for storing large volumes of unstructured content such as media files. S3 also supports pre-signed URLs and direct browser uploads, enabling users to upload files securely without passing through backend servers. EBS volumes (A) are block storage, limited to single AZ, and not suitable for web-scale storage. EFS (B) is a shared file system for POSIX workloads, not for direct browser uploads. S3 Express One Zone (D) offers higher performance for small objects but does not provide cross-AZ durability, making it unsuitable for growing global content. Therefore, option C is the most scalable, durable, and cost-effective solution.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

AWS Secrets Manager is the recommended service for storing database credentials and performing automated rotation. Any Lambda function version or alias can fetch the latest secret value at runtime, ensuring no outdated credentials exist in deployed versions.

Environment variables (Option C) are static per version. Embedding credentials in code (Option B) is insecure and requires redeployment. Parameter Store (Option D) supports rotation but requires more configuration and is not as seamless as Secrets Manager for database credential rotation.

=====================================================

Incremental Exports: Exporting DynamoDB data directly to Amazon S3 provides an automated, serverless way to make data available for analytics without operational overhead.

Analytics-Friendly Storage: Amazon S3 supports long-term analytics workloads and can integrate with tools like Athena or QuickSight.

DynamoDB Export to S3 Documentation

Predictive scaling automatically analyzes historical workload patterns, forecasts future capacity needs, and launches instances ahead of time. AWS documentation states that predictive scaling is designed for workloads with recurring, cyclical patterns—such as scheduled weekly batch jobs.

It also supports "pre-launching" capacity before peak demand. This eliminates manual trend analysis and delivers the lowest operational overhead.

Scheduled scaling (Option B) works but requires manual calculation of capacity numbers and updating if patterns change. Predictive scaling removes this burden entirely.

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A VPC gateway endpoint for Amazon S3 enables private connectivity to S3 without routing traffic through a NAT gateway or over the internet, eliminating NAT gateway costs. This solution is secure and redundant, as S3 endpoints are highly available by design.

AWS Documentation Extract:

"A gateway VPC endpoint enables you to privately connect your VPC to supported AWS services without requiring a NAT gateway or internet gateway."

(Source: Amazon VPC documentation, Gateway Endpoints)

A: NAT instances still incur operational overhead and costs.

B: Internet gateway exposes resources and does not provide private access.

D: Direct Connect is for hybrid networking, not for cost-efficient S3 access.

Amazon Kinesis Data Streams in on-demand mode is purpose-built for real-time ingestion of streaming data and scales automatically with traffic, which is ideal for fluctuating workloads like clickstream data.

Combined with AWS Lambda, which scales concurrently based on incoming events, this setup ensures efficient, real-time processing with minimal configuration and cost overhead.

Option B involves Firehose, which is not optimal for low-latency processing. Option C is incorrect as Kinesis Video Streams is designed for video data, not clickstream. Option D introduces Flink, which adds unnecessary complexity when Lambda suffices.

To securely publish messages to an encrypted Amazon SNS topic, the following steps are required:

A. Resource policy for SNS topic:Ensures that the Lambda function is explicitly allowed to publish messages to the topic.

C. Resource policy for KMS key:Provides the necessary permissions to use the customer-managed key for encryption.

F. Lambda execution role:Grants the Lambda function the necessary IAM permissions to use the encryption key.

Other options:

Bis invalid because using SSE-KMS does not eliminate the need for resource policies.

Doverlaps with A, but specifying the ARN in the topic policy is covered by creating the resource policy.

Eis unrelated as API Gateway is not required for this setup.

AWS Documentation Reference:

Amazon SNS and KMS Permissions