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.

To identify the internal commands that Qlik Replicate is executing prior to an error, theTracelogging level should be used.This level provides detailed information about the operations being performed by Qlik Replicate, including the internal commands executed before an error occurs1.

Here’s how the Trace logging level works in Qlik Replicate:

When logging is set toTrace, the log lines are identified with]T:.This indicates that the log will include detailed trace information about the internal workings of Qlik Replicate, such as sending control records to components or waiting for termination of threads1.

The Trace level is more detailed than Warnings (]W:) and Errors (]E:), which only show warning and error messages without the detailed context of the operations leading up to them1.

The Trace level is also distinct from Verbose (]V:), which provides even more detailed logging information but may not be necessary for identifying the commands leading up to an error1.

Therefore, the correct answer isC. Trace, as it is the appropriate logging level to use when you need to analyze the actions performed by Qlik Replicate just before an error occurs1.

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.

For Qlik Replicate’s Change Data Capture (CDC) process, the valid source endpoint types include:

A. Classic Relational RDBMS: These are traditional relational database management systems that support CDC.Qlik Replicate can capture changes from these systems using log-based CDC tools which are integrated to work with most ETL tools1.

C. SAP ECC and Extractors: SAP ECC (ERP Central Component) and its extractors are also supported as source endpoints for CDC in Qlik Replicate.This allows for the replication of data changes from SAP’s complex data structures1.

The other options provided are not typically associated with CDC in Qlik Replicate:

B. MS Dynamics direct access: While Qlik Replicate can connect to various data sources, MS Dynamics is not commonly listed as a direct source for CDC.

D. Generic REST APIs Data Lake file formats: REST APIs and Data Lake file formats are not standard sources for CDC as they do not maintain transaction logs, which are essential for CDC to track changes.

For detailed information on setting up source endpoints and enabling CDC, you can refer to the official Qlik documentation and community articles that discuss the prerequisites and configurations needed for various source endpoints2345.

To batch-load a source automatically on a daily basis in Qlik Replicate, you would typically use a server scheduler. Here’s how it can be done:

Set trigger through server scheduler (A): You can configure a scheduler on the server where Qlik Replicate is running to trigger the batch load process at a specified time each day.This could be done using the operating system’s built-in task scheduler, such as Windows Task Scheduler or cron jobs on Linux systems1.

The scheduler would execute a command or script that starts the Qlik Replicate task configured for batch loading.The command would utilize Qlik Replicate’s command-line interface or API to initiate the task1.

This approach allows for precise control over the timing of the batch load and can be adjusted to meet the specific scheduling requirements of the data replication process1.

The other options provided are not typically used for setting up an automatic daily batch load:

B. Set trigger through Advanced Run options: While Advanced Run options provide various ways to run tasks, they do not include a scheduling function for daily automation.

C. Set trigger through Task Designer: Task Designer is used for designing and configuring tasks, not for scheduling them.

D. Enable task on full load and apply changes: This option would start the task immediately and is not related to scheduling the task on a daily basis.

Therefore, the verified answer isA. Set trigger through server scheduler, as it is the method that allows for the automation of batch loading on a daily schedule1.

The Apply Exceptions data in Qlik Replicate is retained indefinitely by default. This means thatthe data related to apply exceptions, which includes error records and other relevant information, is not automatically purged after a certain period.

The retention of Apply Exceptions data is crucial for ongoing monitoring and troubleshooting of replication tasks. It allows administrators to review and address any issues that have occurred over the life of the task.

According to the Qlik Replicate documentation, theattrep_apply_exceptionstable, which records processing errors, does not have an automated deletion process.This table includes columns for the task name, table owner, table name, error time (in UTC), statement being executed when the error occurred, and the actual error message1.

This indefinite retention policy ensures that administrators have a complete historical record of all exceptions that have occurred, which can be invaluable for diagnosing and resolving issues with replication tasks. However, it’s important for administrators to manage the size of this table manually to prevent it from growing too large, which could potentially impact system performance.

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.

In Qlik Replicate, when multiple schema rename rules are applied, the final schema name isdetermined by the last applied setting that affects the schema name directly. Here’s how the process works:

Global Transformationto rename schema to ‘USER1’: This rule will initially rename the schema from ‘HR’ to ‘USER1’.However, this is not the final schema name as further changes are applied later in the task settings1.

Task Settings -> Target Metadatato enter the Target table schema as ‘USER2’: This setting overrides the previous global transformation rule and sets the target schema name to ‘USER2’.This is because the Target Metadata settings are applied after the Global Transformations and directly affect the target schema name1.

Global Transformationto add a new column named ‘SCHEMA’ with expression as ‘USER3’: While this rule adds a new column with a value of ‘USER3’, it does not change the schema name itself.It only affects the content of a column within the tables1.

Therefore, the final target schema name, after considering all the applied settings, isD. USER2.This is because the Target Metadata setting in the task settings has the final say on the target schema name, and it is set to 'USER2’1.

According to the Qlik Replicate documentation, the minimum level of permissions required for a user to delete tasks is the Designer role. The predefined roles in Qlik Replicate come with different sets of permissions. The Admin and Designer roles have the permission to delete tasks, while the Operator and Viewer roles do not1.

Here’s a breakdown of the permissions for each role related to task management:

Admin: Can create, design, and delete tasks.

Designer: Can create, design, and delete tasks.

Operator: Can perform runtime operations like start, stop, or reload targets but cannot delete tasks.

Viewer: Can view task history and other details but cannot perform task management operations like deleting tasks.

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.

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.

Qlik Enterprise Manager supports a range of open API methods that allow for programmatic interaction with the system. The supported API methods are:

REST SDK: This provides a RESTful interface for interacting with Qlik Enterprise Manager, allowing for operations such as viewing task details, running tasks, and exporting or importing task definitions12.

.NET SDK: The .NET SDK enables developers to use .NET languages to interact with Qlik Enterprise Manager, facilitating integration with other .NET applications3.

Python SDK: The Python SDK allows for scripting and automation of tasks in Qlik Enterprise Manager using Python, which is particularly useful for data scientists and analysts who prefer Python for data-related tasks3.

These API methods enable automation, integration with enterprise dashboards, and the ability to perform batch operations, among other tasks3.Therefore, the correct answer isC. REST SDK, .NET SDK, Python SDK, as these are the open API methods supported by Qlik Enterprise Manager3.

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.

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.

To address the issue of column names in a source endpoint being too long for the target endpoint’s character limit, the Qlik Replicate administrator should:

D. Define a new Global Transformation rule of the Column type: This allows the administrator to create a rule that applies to all affected columns across all tables.By defining a global transformation rule, the administrator can systematically rename all columns that exceed the character limit1.

The process involves:

Going to the Global Transformations section in Qlik Replicate.

Selecting the option to create a new transformation rule of the Column type.

Using the transformation rule to specify the criteria for renaming the columns (e.g., replacing a prefix or suffix or using a pattern).

Applying the rule to ensure that all affected columns are renamed according to the defined criteria.

The other options are not as efficient or appropriate for solving the issue:

A. Open the Windows command line terminal and run the renamecolumn command: This is not a standard method for renaming columns in Qlik Replicate and could lead to errors if not executed correctly.

B. Visit the Table Settings for each table in a task and select the Transform tab: While this could work, it is not as efficient as defining a global transformation rule, especially if there are many tables and columns to update.

C. Visit the Table Settings for each table and select the Filter tab: The Filter tab is used for record selection conditions and not for renaming columns.

For more detailed instructions on how to define and apply global transformation rules in Qlik Replicate, you can refer to the official Qlik documentation onGlobal Transformations.