QREP Qlik Replicate Certification Exam Questions and Answers

 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 timeout 1 .

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 transaction 2 .

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 is C. 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 Replicate 2 1 .

The Provider Syntax in Qlik Replicate is used to define and modify certain behaviors and properties of the replication process. Specifically, it allows for the creation of a custom syntax for various operations and mappings. The information that can be stored and modified in the Provider Syntax includes:

Data Type Mapping (D) : This involves creating mappings between the provider data types and Attunity Replicate data types. It allows for the customization of how data types are handled during the replication process 1 .

The other options provided do not align with the capabilities of the Provider Syntax:

A. Endpoint definition - Servername and port number : This information is typically configured in the endpoint settings, not within the Provider Syntax.

B. Username and permission of the endpoints : Usernames and permissions are also configured in the endpoint settings or within the security configurations of the respective systems.

C. Schema definition of the endpoints : While the schema definition is crucial for replication, it is not something that is typically modified within the Provider Syntax.

Therefore, the verified answer is D. Data Type Mapping , as it is the piece of information that is stored and can be modified within the Provider Syntax according to the Qlik Replicate documentation 2 1 .

 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 logging at 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 correctly 1 .

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 purposes 1 .

After enabling supplemental logging, verify that it is active by querying the v$database view:

SELECT supplemental_log_data_min FROM v$database;

The correct return value should be ‘YES’, indicating that supplemental logging is enabled 1 .

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 is C. Enable supplemental logging , as it directly addresses the requirement to fix the error related to supplemental logging in Qlik Replicate 2 1 .

For tasks involving Kafka as a target in Qlik Replicate, the valid options are:

A. Full Load and Apply Change : This combination is valid because Kafka can be used both for initial full loads of data and for applying changes captured through CDC (Change Data Capture). In a task with a Kafka target endpoint, each source record is transformed into a message which is then written to a partition in the specified topic 1 .

The other options are not typically used with Kafka in Qlik Replicate:

B. Full Load and Stage Change : Staging changes is not a standard task option when using Kafka as a target.

C. Apply Change and Store Change : While Kafka can be used to apply changes, the “Store Change” option is not a recognized task option for Kafka targets.

D. Full Load and Store Change : Similarly, “Store Change” is not a standard task option for Kafka targets.

For more information on how to set up and use Kafka as a target endpoint in Qlik Replicate, including the configuration of Full Load and Apply Change tasks, you can refer to the official Qlik community articles and support resources 2 1 .

When a Qlik Replicate administrator needs to access data from a CRM application, the most efficient and direct method is often through the application’s REST API. Here’s why:

Connect to the REST API provided by the application © : Many modern CRM applications provide a REST API for programmatic access to their data. This method is typically supported by data integration tools like Qlik Replicate and allows for a more seamless and real-time data extraction process. The REST API can provide a direct and efficient way to access the required data without the need for intermediate steps 1 .

Connect directly to the application (A) : While this option might seem straightforward, it is not always possible or recommended due to potential limitations in direct application connections or the lack of a suitable interface for data extraction.

Export tables to CSVs in a shared folder and connect to that (B) : This method involves additional steps and can be less efficient. It requires manual intervention to export the data and does not support real-time data access.

Connect to the underlying RDBMS (D) : Accessing the underlying relational database management system (RDBMS) can be an option, but it may bypass the business logic implemented in the CRM application and could lead to incomplete or inconsistent data extraction.

Given these considerations, the REST API method © is generally the preferred approach for accessing CRM application data in a structured and programmable manner, which aligns with the capabilities of Qlik Replicate 2 1 3 .

Qlik Enterprise Manager provides a centralized command center to configure, execute, and monitor data replication and transformation tasks across the enterprise. It is specifically designed to manage and control Qlik Replicate and Qlik Compose tasks. Additionally, it integrates with Qlik Catalog to automatically catalog data assets generated by Qlik Replicate directly in Qlik Catalog 1 . This integration allows for tracking end-to-end data lineage, which improves compliance, governance, and trust in the data assets managed within Qlik Catalog 1 .

The documentation clearly states that Qlik Enterprise Manager is used to design, execute, and monitor Qlik Replicate and Qlik Compose tasks, and it also mentions the integration with Qlik Catalog for data asset management 2 . However, there is no mention of Qlik Sense being controlled by Qlik Enterprise Manager. Qlik Sense is a separate product for data visualization and analytics, and its management is not within the scope of Qlik Enterprise Manager’s functionalities as described in the available resources 1 2 .

Therefore, the correct answer is C. Qlik Replicate, Qlik Compose, Qlik Catalog , as these are the components that can be controlled with Qlik Enterprise Manager.

Qlik Replicate supports a variety of task types to accommodate different data replication needs. The three task types supported are:

LogStream to Staging Folder (A) : This task type allows Qlik Replicate to save data changes from the source database transaction log to a staging folder. These changes can then be applied to multiple targets 1 .

Full load, apply, and store change (E) : This is a comprehensive task type that includes a full load of the source database, applying changes to the target, and storing changes in an audit table on the target side 1 .

LogStream full load (F) : Similar to the LogStream to Staging Folder, this task type involves saving data changes from the source database transaction log. However, it also includes a full load of the data to the target database 1 .

The other options provided do not align with the task types supported by Qlik Replicate:

B. Store changes bidirectional : While Qlik Replicate supports bidirectional tasks, the option as stated does not accurately describe a supported task type.

C. LogStream store changes : This option is not clearly defined as a supported task type in the documentation.

D. Scheduled full loads : Although Qlik Replicate can perform full loads, “Scheduled full loads” as a specific task type is not mentioned in the documentation.

Therefore, the verified answers are A , E , and F , as they represent the task types that Qlik Replicate supports according to the official documentation 1 .

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

Navigate to the Global Transformations section 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 the Schema option 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 task 1 2 .

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 Filter and C. 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 is D. Global Transformations - Schema , as it is the correct method to set all incoming columns to match a single schema in Qlik Replicate 1 2 .

When using LogStream staging for replication in Qlik Replicate, the Staging folder is compressed and stored on the Qlik Replicate server itself. Here’s the process:

A Log Stream Staging task is defined to replicate changes from the source endpoint to the Log Stream Staging folder 1 .

This task creates a “Staging” file in the Log Stream Staging folder, which contains the changes from the source database transaction log as a compressed binary file 1 .

The storage path for the streamed data, which includes the Staging folder, must be specified in the Log Stream target endpoint settings, and it is explicitly stated that this path should be located on the Replicate Server machine 2 .

Therefore, the correct answer is D. On the Qlik Replicate server , as the staging folder is located on the machine where Qlik Replicate server is running, and it is where the compressed staging files are stored 1 2 .

Questions no: 17 Verified Answer : = A. IBM IMS & E. HP NonStop Step 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 agents 1 .

HP NonStop (E) : This is a platform for high-availability servers and is also supported by ARC CDC agents 1 .

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 are A. IBM IMS and E. HP NonStop , as they are the endpoints that utilize ARC CDC agents for capturing changes 1 .

In a scenario where an operative database can commit only two engines to Qlik Replicate for initial loads, the task settings should be modified to ensure that no more than two tables are loaded at any given time. This can be achieved by:

C. Apply Full Load Settings to limit the number of engines to two : This setting allows you to specify the maximum number of concurrent table loads during the Full Load operation. By limiting this number to two, you ensure that the operative database’s capacity is not exceeded 1 .

The other options are not suitable because:

A. Apply Change Processing Tuning : This option is related to the CDC (Change Data Capture) phase and not the initial Full Load phase. Increasing the apply batched changes interval would not limit the number of engines used during the Full Load.

B. Qlik Replicate tasks only load one table at a time by default : This statement is not accurate as Qlik Replicate can be configured to load multiple tables concurrently, depending on the task settings.

D. Apply Full Load Tuning to read a maximum number of tables not greater than two : While this option seems similar to the correct answer, it is not a recognized setting in Qlik Replicate’s configuration options.

For detailed guidance on configuring task settings in Qlik Replicate, particularly for managing the number of concurrent loads, you can refer to the official Qlik community articles on Qlik Replicate Task Configuration Options .

When encountering table errors in Qlik Replicate, the escalation policy is set to Stop Task and 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 continues 1 .

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 errors 1 .

After a Qlik Replicate administrator creates a new task, runs a Full Load test, and turns on Change Processing, the method to test this task would be to:

Stop the task : This ensures that no active processes are running that could interfere with the testing.

Run - > Advanced Run Options : This step allows the administrator to access more specific settings that can control how the task is executed.

Start Change Processing Only : This option will start the Change Data Capture (CDC) phase without re-running the Full Load. It’s used to test the CDC mechanism specifically, ensuring that changes made at the source are captured and applied to the target correctly.

This method is supported by best practices in the Qlik community and documentation, which suggest using the Advanced Run Options to control the task’s behavior for testing purposes 1 . It allows the administrator to verify that the Change Processing is set up correctly and is functioning as expected without the need to reload the target or recreate the task entirely. This approach is efficient and effective for testing the CDC functionality in isolation.

The default port for Qlik Replicate Server on Linux is 3552 . 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 system 1 .

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 is D. 3552 , as it is the port designated for Qlik Replicate Server on Linux according to the official documentation 1 .

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 the repctl exportrepository command, 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 the repctl importrepository command, allowing for easy migration or restoration of tasks 2 .

This JSON file contains everything required to reconstruct the data replication project, making it an essential tool for administrators managing Qlik Replicate tasks 3 .

Therefore, the correct answer is D. Task JSON files , as they are the files that can be exported and imported in Qlik Replicate for the mentioned purposes 1 2 3 .