Qlik Qlik Replicate Certification Exam QREP Exam Dumps: Updated Questions & Answers (October 2025)

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.

To add a new column to a single table in a Qlik Replicate task, the correct path is throughTable Settings. Here’s the process you would typically follow:

Navigate to theTable Settingsof the table you wish to modify within your task.

Go to theGeneralsection.

Use the option toAdd New Column.

This process allows you to add a column directly to the table’s schema as part of the task configuration. It’s important to note that this action is part of the task’s design phase, where you can specify the schema changes that should be applied to the data as it is replicated.

The other options listed, such asNew TransformationorSelect Table -> Transform, are not the direct paths for adding a new column to a table’s schema within a task.They are related to different aspects of task configuration and transformation1.

When configuring Oracle as a source endpoint for Qlik Replicate, there are several key prerequisites that need to be met:

Enable supplemental logging (A): Supplemental logging is crucial for capturing the changes in the Oracle database accurately, especially for Change Data Capture (CDC) operations.It ensures that all necessary column data is logged for replication purposes1.

Install the Oracle Instant Client (B): The Oracle Instant Client provides the necessary libraries for Qlik Replicate to connect to and interact with the Oracle database.It’s required for the proper functioning of Qlik Replicate2.

Enable ARCHIVELOG mode (D): ARCHIVELOG mode is necessary for the Oracle database to archive redo logs, which Qlik Replicate uses to capture changes.This mode allows the database to continue functioning and preserve the logs even after a log switch, which is essential for CDC1.

The other options provided are not listed as key prerequisites for configuring Oracle as a source endpoint in Qlik Replicate:

C. Complete a full backup of the source: While it’s a good practice to have a backup, it’s not a prerequisite for configuring the source endpoint.

E. Provide Oracle read-only privileges: Read-only privileges might be necessary for certain operations, but they are not listed as a key prerequisite.

F. Configure Oracle Recovery Model: This is not mentioned as a prerequisite in the Qlik Replicate documentation.

Therefore, the verified answers areA,B, andD, as they represent the necessary steps to configure Oracle as a source endpoint in Qlik Replicate12.

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 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 steps1.

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 Replicate213.

For stream endpoint types like Kafka or Azure Event Hubs in Qlik Replicate, there are specific limitations that apply to the replication options and target table preparation options:

D. The Store Changes replication option is not supported: This limitation is explicitly mentioned for Kafka1and Azure Event Hubs23. The Store Changes mode is not supported when using these stream endpoints, meaning that changes cannot be stored for later retrieval or reporting.

E. The DROP and CREATE table target table preparation option is not supported: This is also a known limitation for Kafka as a target endpoint1. The Drop and Create table Target Table Preparation option is not supported, which affects how tables are prepared on the target side during replication.

The other options are not correct because:

A. The Apply Changes replication option is not supported: This is not listed as a limitation for Kafka or Azure Event Hubs.

B. The Full Load replication option is not supported: Full Load is supported for Kafka1.

C. Associated tasks filling those endpoint types cannot be stopped: This is not mentioned as a limitation, and tasks can typically be stopped unless otherwise specified.

For more detailed information on the limitations of using Kafka or Azure Event Hubs as target endpoints in Qlik Replicate, you can refer to the official Qlik documentation123.

When performing a Full Load to a target endpoint in Qlik Replicate, the task logging components that are associated with this process areTARGET_LOADandSOURCE_UNLOAD.

TARGET_LOAD: This component is responsible for loading the data into the target endpoint.It represents the process where Qlik Replicate reads all columns/rows from the Source database and creates the exact same copy on the Target database1.

SOURCE_UNLOAD: This component is involved in unloading the data from the source endpoint.It is part of the Full Load process where the data is read from the source and prepared for transfer to the target2.

The other options provided are not directly associated with the Full Load process to a target endpoint:

TARGET_APPLYis related to the Change Data Capture (CDC) phase where changes from the source are applied to the target2.

FILE_TRANSFERis not a term directly associated with Qlik Replicate’s logging components.

STREAMrefers to the Log Stream tasks, which is a different type of task configuration used for saving data changes from the transaction log of a single source database and applying them to multiple targets2.

For a comprehensive understanding of the task types and options in Qlik Replicate, you can refer to the official Qlik community articles onQlik Replicate Task Configuration OptionsandAn Introduction to Qlik Replicate Tasks: Full Load vs CDC.

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.

For replicating columns that contain large PDFs, the appropriate data type to use in Qlik Replicate is:

D. BLOB: This stands for Binary Large Object and is the data type used for storing large binary data such as PDF files.The BLOB data type is suitable for handling the size and binary nature of PDF files1.

The other options are not suitable for the following reasons:

A. DLOB: This is not a recognized data type in Qlik Replicate.

B. NCLOB: NCLOB, or National Character Large Object, is used for storing large text data in the national character set and is not optimal for binary data like PDFs.

C. NVARCHAR(1000): NVARCHAR is a character data type with a specified length, which would not be suitable for large binary objects like PDFs due to size limitations and the nature of the data.

For more information on data types and their usage in Qlik Replicate, you can refer to the official Qlik Replicate documentation onReplicate data types.

The server logs in Qlik Replicate provide information about the Qlik Replicate Server instance, rather than individual tasks.The logs can include various levels of information, such as errors, warnings, info, trace, and verbose details1. Specifically, the server logs can provide insights into:

Network and performance issues: These might be indicated by error or warning messages related to connectivity or performance bottlenecks.

Load status and performance of task: While the server logs focus on the server instance, they may contain information about the overall load status and performance, especially if there are server-level issues affecting tasks.

Specific task information: The server logs can include information about tasks, particularly if there are errors or warnings that pertain to task execution at the server level.

Qlik Replicate Server status: This includes general information about the server’s health, status, and any significant events that affect the server’s operation.

Therefore, while the server logs can potentially contain a range of information, the primary purpose is to provide details on theQlik Replicate Server status(D), including any issues that may impact the server’s ability to function properly and manage tasks231.