Qlik QREP Dumps

When a Qlik Replicate developer encounters errors about a duplicate key when applying INSERT, the first step to identify and resolve the issue is to:

A. Check the error message in the Apply Exceptions control table: This control table contains detailed information about any exceptions that occur during the apply process, including duplicate key errors.By examining the error messages, the developer can understand the cause of the issue and take appropriate action to resolve it1.

The process involves:

Accessing the Qlik Replicate Console.

Navigating to the task that is experiencing the issue.

Opening the Apply Exceptions control table to review the error messages related to the duplicate key issue.

Analyzing the error details to determine the cause, such as whether it’s due to a source data problem or a target schema constraint.

The other options are not the correct initial steps for identifying the issue:

B. Stop and reload the task: This action might temporarily bypass the error but does not address the root cause of the duplicate key issue.

C. Stop task and enable the Apply Exceptions control table: The Apply Exceptions control table should already be enabled and checked for errors as the first step.

D. Stop and resume the task: Resuming the task without identifying the cause of the error will likely result in the error reoccurring.

For more information on how to troubleshoot and handle duplicate key errors in Qlik Replicate, you can refer to the official Qlik community articles and support resources that provide guidance on error handling and the use of the Apply Exceptions control table2.

Questions no:16Verified Answer: = C. Create a task streaming to a streaming target endpoint (e.g., Kafka) and consume that endpoint in the following tasks as a source endpoint

Step by Step Comprehensive and Detailed Explanation with all References: =To deliver data from a source endpoint with minimal impact and distribute it to several target endpoints in Qlik Replicate, the best approach is:

C. Create a task streaming to a streaming target endpoint (e.g., Kafka) and consume that endpoint in the following tasks as a source endpoint: This method allows for efficient data distribution with minimal impact on the source system.By streaming data to a platform like Kafka, which is designed for high-throughput, scalable, and fault-tolerant storage, Qlik Replicate can then use this data stream as a source for multiple downstream tasks12.

The other options are less optimal because:

A. Create a LogStream task followed by multiple tasks using an endpoint that reads changes from the log stream staging folder: While this option involves a LogStream, it does not specify streaming to a target endpoint that can be consumed by multiple tasks, which is essential for minimal impact distribution.

B. Create a task streaming to a dedicated buffer database (e.g., Oracle or MySQL) and consume that database in the following tasks as a source endpoint: This option introduces additional complexity and potential performance overhead by using a buffer database.

D. Create multiple tasks using the same source endpoint: This could lead to increased load and impact on the source endpoint, which is contrary to the requirement of minimal impact.

For more detailed information on how to set up streaming tasks to target endpoints like Kafka and how to configure subsequent tasks to consume from these streaming endpoints, you can refer to the official Qlik documentation onAdding and managing target endpoints.

When encountering table errors in Qlik Replicate, the escalation policy is set toStop Taskand cannot be changed. This means that if the number of table errors reaches a specified threshold, the task will automatically stop, requiring manual intervention to resolve the issue.

The escalation action for table errors is specifically designed to halt the task to prevent further errors or data inconsistencies from occurring.This is a safety measure to ensure that data integrity is maintained and that any issues are addressed before replication continues1.

The other options listed are not escalation actions for table errors:

A. Log Record to the Exceptions Table: While logging errors to the exceptions table is a common action, it is not an escalation action.

B. No Escalate Action: This is not a valid option as there is a specific escalation action defined for table errors.

C. Suspend Table: Suspending a table is a different action that can be taken in response to errors, but it is not the defined escalation action for table errors in Qlik Replicate.

For more information on error handling and escalation actions in Qlik Replicate, you can refer to the official Qlik Replicate Help documentation, which provides detailed guidance on configuring error handling policies and actions for various types of errors1.

The SOURCE_UNLOAD component is responsible for reading data from the source endpoint.

The TARGET_LOAD component is responsible for writing the data to the target endpoint.

These components work in tandem during the Full Load replication process to move data from the source to the target. According to Qlik Replicate documentation, these two components are crucial in handling the extraction and loading phases of Full Load replication.

In the context of Full Load replication with Qlik Replicate, the components responsible for reading data from the source and writing it to the target are:

SOURCE_UNLOAD: This component is responsible for unloading data from the source endpoint.It extracts the data that needs to be replicated to the target system1.

TARGET_LOAD: This component is in charge of loading the data into the target endpoint.After the data is extracted by the SOURCE_UNLOAD, the TARGET_LOAD component ensures that the data is properly inserted into the target system1.

The other options provided do not align with the Full Load replication process:

B. TARGET_APPLYandD. SOURCE_CAPTUREare typically associated with the Change Data Capture (CDC) process, not the Full Load process2.

C. TARGET_UNLOADis not a recognized component in the context of Qlik Replicate’s Full Load replication.

Therefore, the correct answers areA. SOURCE_UNLOADandE. TARGET_LOAD, as they are the components that handle the reading and writing of data during the Full Load replication process12.

To determine the minimum role permission required for a user to share status on Tasks and Server activity in Qlik Replicate, we can refer to the official Qlik Replicate documentation. According to the documentation, there are four predefined roles available: Admin, Designer, Operator, and Viewer. Each role has its own set of permissions.

The Viewer role is the most basic role and provides the user with the ability to view task history, which includes the status on Tasks and Server activity.This role does not allow the user to perform any changes but does allow them to share information regarding the status of tasks and server activity1.

Here is a breakdown of the permissions for the Viewer role:

View task history: Yes

Download a memory report: No

Download a Diagnostics Package: No

View and download log files: No

Perform runtime operations (such as start, stop, or reload targets): No

Create and design tasks: No

Edit task description in Monitor View: No

Delete tasks: No

Export tasks: No

Import tasks: No

Change logging level: No

Delete logs: No

Manage endpoint connections (add, edit, duplicate, and delete): No

Open the Manage Endpoint Connections window and view the following endpoint settings: Name, type, description, and role: Yes

Click the Test Connection button in the Manage Endpoint Connections window: No

View all of the endpoint settings in the Manage Endpoint Connections window: No

Edit the following server settings: Notifications, scheduled jobs, and executed jobs: No

Edit the following server settings: Mail server settings, default notification recipients, license registration, global error handling, log management, file transfer service, user permissions, and resource control: No

Specify credentials for running operating system level post-commands on Replicate Server: No

Given this information, the Viewer role is sufficient for a user who needs to share status on Tasks and Server activity, making it the minimum role permission required for this purpose1.

In Change Data Capture (CDC) mode, Batch optimized apply mode can be set based on time and/or volume.

This means that the batching of transactions can be controlled by specifying time intervals or the volume of data changes to be batched together.

This optimization helps improve performance by reducing the frequency of writes to the target system and handling large volumes of changes efficiently. The Qlik Replicate documentation outlines this option as a method to enhance the efficiency of data replication in CDC mode by batching transactions based on specific criteria.

In the Change Data Capture (CDC) mode of a Qlik Replicate task, when using the Batch optimized apply mode, the system allows for tuning based on time and/or volume. This setting is designed to optimize the application of changes in batches to the target system. Here’s how it works:

Time: You can set intervals at which batched changes are applied.This includes setting a minimum amount of time to wait between each application of batch changes, as well as a maximum time to wait before declaring a timeout1.

Volume: The system can be configured to force apply a batch when the processing memory exceeds a certain threshold.This allows for the consolidation of operations on the same row, reducing the number of operations on the target to a single transaction2.

The other options provided do not align with the settings for Batch optimized apply mode in CDC tasks:

A. Source connection processes: This is not a setting related to the batch apply mode.

B. Number of changed records: While the number of changed records might affect the batch size, it is not a setting that can be directly configured in this context.

D. Maximum time to batch transactions: This option is related to the time aspect but does not fully capture the essence of the setting, which includes both time and volume considerations.

Therefore, the verified answer isC. Time and/or volume, as it accurately represents the options that can be set for Batch optimized apply mode in the CDC tasks of Qlik Replicate21.

The default port for Qlik Replicate Server on Linux is3552. This port is used for outbound and inbound communication unless it is overridden during the installation or configuration process. Here’s a reference to the documentation that confirms this information:

The official Qlik Replicate documentation states that “Port 3552 (the default rest port) needs to be opened for outbound and inbound communication, unless you override it as described below.” This indicates that 3552 is the default port that needs to be considered during the installation and setup of Qlik Replicate on a Linux system1.

The other options provided do not correspond to the default port for Qlik Replicate Server on Linux:

A. 3550: This is not listed as the default port in the documentation.

B. 443: This is commonly the default port for HTTPS traffic, but not for Qlik Replicate Server.

C. 80: This is commonly the default port for HTTP traffic, but not for Qlik Replicate Server.

Therefore, the verified answer isD. 3552, as it is the port designated for Qlik Replicate Server on Linux according to the official documentation1.

Qlik Replicate offers several methods for parallel load during a full load process to accelerate the replication of large tables by splitting the table into segments and loading these segments in parallel. The three primary ways Qlik Replicate allows parallel loading are:

Use Data Ranges:

This method involves defining segment boundaries based on data ranges within the columns. You can select segment columns and then specify the data ranges to define how the table should be segmented and loaded in parallel.

Use Partitions - Use all partitions - Use main/sub-partitions:

For tables that are already partitioned, you can choose to load all partitions or use main/sub-partitions to parallelize the data load process. This method ensures that the load is divided based on the existing partitions in the source database.

Use Partitions - Specify partitions/sub-partitions:

This method allows you to specify exactly which partitions or sub-partitions to use for the parallel load. This provides greater control over how the data is segmented and loaded, allowing for optimization based on the specific partitioning scheme of the source table.

These methods are designed to enhance the performance and efficiency of the full load process by leveraging the structure of the source data to enable parallel processing

In Qlik Replicate, tasks can be exported and imported for various purposes such as remote backup, migration, troubleshooting, and configuration updates. The format used for these operations is the JSON file format. Here’s how the process works:

To export tasks, you can use therepctl exportrepositorycommand, which generates a JSON file containing all task definitions and endpoint information (except passwords)1.

The generated JSON file can then be imported to a new server or instance of Qlik Replicate using therepctl importrepositorycommand, allowing for easy migration or restoration of tasks2.

This JSON file contains everything required to reconstruct the data replication project, making it an essential tool for administrators managing Qlik Replicate tasks3.

Therefore, the correct answer isD. Task JSON files, as they are the files that can be exported and imported in Qlik Replicate for the mentioned purposes123.

The goal is to convert a timestamp to Unix time (seconds since January 1, 1970).

Thestrftimefunction is used to format date and time values.

To get the Unix epoch time, you can use the command:strftime('%s',SAR_H_COMMIT_TIMESTAMP) - strftime('%s','1970-01-01 00:00:00').

This command extracts the Unix time from the timestamp and subtracts the Unix epoch start time to get the number of seconds since January 1, 1970. This is consistent with the Qlik Replicate documentation and SQL standard functions for handling date and time conversions.

To convert a timestamp to Unix time (also known as Unix epoch time), which is the number of seconds since January 1st, 1970, you can use thestrftimefunction with the%sformat specifier in Qlik Replicate. The correct syntax for this conversion is:

strftime('%s', SAR_H_COMMIT_TIMESTAMP) - strftime('%s','1970-01-01 00:00:00')

This function will return the number of seconds between theSAR_H_COMMIT_TIMESTAMPand the Unix epoch start date. Here’s a breakdown of the function:

strftime('%s', SAR_H_COMMIT_TIMESTAMP)converts theSAR_H_COMMIT_TIMESTAMPto Unix time.

strftime('%s','1970-01-01 00:00:00')gives the Unix time for the epoch start date, which is0.

Subtracting the second part from the first part is not necessary in this case because the Unix epoch time is defined as the time since1970-01-01 00:00:00. However, if the timestamp is in a different time zone or format, adjustments may be needed.

The other options provided do not correctly represent the conversion to Unix time:

Options A and B usedatetimeinstead ofstrftime, which is not the correct function for this operation1.

Option C incorrectly includesdatetime.datetime, which is not a valid function in Qlik Replicate and seems to be a mix of Python code and SQL1.

Option E usesTime.now.strftime, which appears to be Ruby code and is not applicable in the context of Qlik Replicate1.

Therefore, the verified answer isD, as it correctly uses thestrftimefunction to convert a timestamp to Unix time in Qlik Replicate1.

When a new column is added to one of the tables in a Qlik Replicate task, the administrator should stop and then resume the task to replicate this change. This process allows Qlik Replicate to recognize the structural change and apply it accordingly.

The steps involved in this process are:

Stop the task: This ensures that no data changes are missed during the schema change.

Resume the task: Once the task is resumed, Qlik Replicate will pick up the DDL change and apply the new column to the target system.

This procedure is supported by the Qlik Replicate’s DDL handling policy, which can be set to perform an “alter target table” when the source table is altered.This means that when the task is resumed, the new columns from the source tables will be added to the Replicate target1.

It’s important to note that while stopping and resuming the task is generally the recommended approach, the exact steps may vary depending on the specific configuration and version of Qlik Replicate being used. Therefore, it’s always best to consult the latest official documentation or support resources to ensure the correct procedure for your environment.

The error message "Failed adding supplemental logging for table" indicates that supplemental logging is not enabled on the Oracle source.

Supplemental logging must be enabled to capture the necessary changes for replication.

To fix this error, you should enable supplemental logging on the Oracle database for the specific table or tables.

This can usually be done by executing the following SQL command on the Oracle source:

ALTER DATABASE ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;

Verify that the logging is enabled and then retry the replication task. This solution aligns with the troubleshooting steps provided in the Qlik Replicate documentation for dealing with supplemental logging errors.

The error message “Failed adding supplemental logging for table ‘Table name’” indicates that supplemental logging has not been enabled for the table in the Oracle source database. Supplemental logging is necessary for Qlik Replicate to capture the changes in the Oracle database accurately, especially for Change Data Capture (CDC) operations.

To resolve this error, you should:

Enable supplemental loggingat the database level by executing the following SQL command in the Oracle database:

ALTER DATABASE ADD SUPPLEMENTAL LOG DATA;

This command enables minimal supplemental logging, which is required for Qlik Replicate to function correctly1.

If you need to enable supplemental logging for all columns, you can use the following SQL command:

ALTER DATABASE ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;

This ensures that all necessary column data is logged for replication purposes1.

After enabling supplemental logging, verify that it is active by querying thev$databaseview:

SELECT supplemental_log_data_min FROM v$database;

The correct return value should be ‘YES’, indicating that supplemental logging is enabled1.

The other options provided are not directly related to the issue of supplemental logging:

A. Contact the Oracle DBA: While contacting the DBA might be helpful, the specific action needed is to enable supplemental logging.

B. Check the permission on the target endpoint: Permissions on the target endpoint are not related to the supplemental logging requirement on the source database.

D. Check the permission of the source endpoint: Permissions on the source endpoint are important, but the error message specifically refers to the need for supplemental logging.

Therefore, the verified answer isC. Enable supplemental logging, as it directly addresses the requirement to fix the error related to supplemental logging in Qlik Replicate21.

Questions no:17Verified Answer: = A. IBM IMS & E. HP NonStopStep by Step Comprehensive and Detailed Explanation with all References: =ARC (Attunity Replicate Connect) CDC agents are used for capturing changes (CDC) and can be utilized with both relational and non-relational endpoints supported by ARC. The endpoints that have ARC CDC agents include:

IBM IMS (A): This is a database and transaction management system, and it is listed as one of the endpoints supported by ARC CDC agents1.

HP NonStop (E): This is a platform for high-availability servers and is also supported by ARC CDC agents1.

The other options provided do not align with the endpoints that have ARC CDC agents:

B. IBMDB2Z/OS: While DB2 for z/OS is a common database system, it is not mentioned in the context of ARC CDC agents.

C. Kafka Source: Kafka is a streaming platform, and while it can be an endpoint for data, it is not listed as having ARC CDC agents.

D. SAPHANA: SAP HANA is an in-memory database, and it is not specified as having ARC CDC agents.

Therefore, the verified answers areA. IBM IMSandE. HP NonStop, as they are the endpoints that utilize ARC CDC agents for capturing changes1.

To run daily reports about the changes that have occurred within the past 24 hours using Qlik Replicate, the option that must be set isStore Changes. This feature enables Qlik Replicate to keep a record of the changes that have occurred over a specified period, which in this case is the past 24 hours.

B. Store Changes: This setting allows Qlik Replicate to capture and store the changes made to the data in the source system.These stored changes can then be used to generate reports that reflect the data modifications within the desired timeframe1.

The other options are not specifically designed for the purpose of running daily change reports:

A. Apply Changes: This option is related to applying the captured changes to the target system, which is a different stage of the replication process.

C. Stage Changes: Staging changes involves temporarily storing the changes before they are applied to the target, which is not the same as storing changes for reporting purposes.

D. Full Load: The Full Load option is used to replicate the entire dataset from the source to the target, which is not necessary for generating reports based on changes within a specific timeframe.

For more information on how to configure the Store Changes option and generate reports based on the stored changes, you can refer to the official Qlik documentation and community discussions that provide insights into best practices for setting up replication tasks and managing change data2.

To run Analytics under Qlik Enterprise Manager, it is required to have aPostgreSQL Databaseinstalled. This is because the Analytics data for Qlik Enterprise Manager is stored in a PostgreSQL database.Before using the Analytics feature, you must ensure that PostgreSQL (version 12.16 or later) is installed either on the Enterprise Manager machine or on a machine that is accessible from Enterprise Manager1.

Here are the steps and prerequisites for setting up Analytics in Qlik Enterprise Manager:

Install PostgreSQL: The setup file for PostgreSQL is included with Enterprise Manager, and it must be installed to store the Analytics data1.

Create a dedicated database and user: A dedicated database and user in PostgreSQL should be created, which will own the tables accessed by the Enterprise Manager Analytics module1.

Configure connectivity: Connectivity to the PostgreSQL repository must be configured as described in the Repository connection settings1.

Data collection and purging: Configure data collection and purging settings as described in the Analytics - Data collection and purge settings1.

Register a license: A Replication Analytics license is required to use Analytics.If you have a license, you can register it by following the procedure described in Registering a license1.

The other options provided, such as Qlik Replicate (A), Qlik Compose (B), and both Qlik Compose and Replicate (D), are not components that must be installed to run Analytics under Enterprise Manager.The essential component is the PostgreSQL Database ©, which serves as the backend for storing the Analytics data1.

Therefore, the verified answer isC. PostgreSQL Database, as it is the required component to run Analytics under Qlik Enterprise Manager1.

To download the task diagnostic package in Qlik Replicate, you need to follow these steps:

Open the task from the overview in the Qlik Replicate Console.

Switch to theMonitorview.

Click on theToolstoolbar button.

Navigate toSupport.

SelectDownload Diagnostic Package1.

This process will generate a task-specific diagnostics package that contains the task log files and various debugging data that may assist in troubleshooting task-related issues. Depending on your browser settings, the file will either be automatically downloaded to your designated download folder, or you will be prompted to download it.The file will be named in the format__diagnostics__.zip12.

The other options provided do not accurately describe the process for downloading a diagnostic package in Qlik Replicate:

Bis incomplete and does not provide a valid path.

Cincorrectly suggests going to server settings and logging, which is not the correct procedure.

Dsuggests a method that is not documented in the official Qlik Replicate help resources.

Therefore, the verified answer isA, as it correctly outlines the steps to download a diagnostic package in Qlik Replicate12.

The Qlik Replicate diagnostic package is designed to assist in troubleshooting task-related issues. When you generate a task-specific diagnostics package, it includes the task log files and various debugging data. The contents of the diagnostics package are crucial for the Qlik Support team to review and diagnose any problems that may arise during replication tasks.

According to the official Qlik documentation, the diagnostics package contains:

Task log files

Various debugging data

While the documentation does not explicitly list “Statistics, Task Status, and Metadata” as part of the diagnostics package, these elements are typically included in the debugging data necessary for comprehensive troubleshooting.Therefore, the closest match to the documented contents of the diagnostics package would be option C, which includes Logs, Statistics, Task Status, and Metadata123.

It’s important to note that the specific contents of the diagnostics package may vary slightly based on the version of Qlik Replicate and the nature of the task being diagnosed. However, the provided answer is based on the most recent and relevant documentation available.

To set all incoming columns to match a single schema in Qlik Replicate, an administrator should use theGlobal Transformationsfeature. Here’s the process:

Navigate to theGlobal Transformationssection within the Qlik Replicate task settings.

Within Global Transformations, there is an option to define transformations that apply to all tables and columns being replicated.

Use theSchemaoption within Global Transformations to specify the target schema for all incoming columns.

This approach ensures that all incoming data conforms to a predefined schema, which is particularly useful when consolidating data from multiple sources into a single target schema.It allows for the standardization of column names, data types, and other schema-related attributes across all tables involved in the replication task12.

The other options provided do not directly address the requirement to set all incoming columns to match a single schema:

A. Table Selection - Schema: This option is more about selecting which tables and schemas to include in the replication task, rather than defining a global schema for all columns.

B. Global Transformations - Add FilterandC. Add Filter - Schema: While filters are used to specify conditions for data transformation or selection, they do not provide a means to globally set the schema for all incoming columns.

Therefore, the verified answer isD. Global Transformations - Schema, as it is the correct method to set all incoming columns to match a single schema in Qlik Replicate12.