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Questions no: 21 Verified Answer: = C. As close as possible to the source system

Step by Step Comprehensive and Detailed Explanation with all Reference: = In an on-premises environment, Qlik Replicate should be set up as close as possible to the source system. This is because the source system is where the initial capture of data changes occurs, and having Qlik Replicate close to the source helps to minimize latency and maximize the efficiency of data capture.

C . As close as possible to the source system: Positioning Qlik Replicate near the source system reduces the time it takes to capture and process changes, which is critical for maintaining low latency in replication tasks1.

The other options are not recommended because:

A . As close as possible to the target system: While proximity to the target system can be beneficial for the apply phase, it is more crucial to have minimal latency during the capture phase, which is closer to the source.

B . In the ''middle'' between the source and target: This does not provide the optimal configuration for either the capture or apply phases and could introduce unnecessary complexity and potential latency.

D . In a cloud environment: This option is not relevant to the question as it specifies an on-premises setup. Additionally, whether to use a cloud environment depends on the specific architecture and requirements of the replication scenario.

For detailed guidance on setting up Qlik Replicate in an on-premises environment, including considerations for placement and configuration to optimize performance and reduce latency, you can refer to the official Qlik Replicate Setup and User Guide1.

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 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 Replicate21.

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.

The Apply Exceptions data in Qlik Replicate is retained indefinitely by default. This means that the 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, the attrep_apply_exceptions table, 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 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 on Global Transformations.