QREP Exam Dumps - Qlik Replicate Certification Exam

In Qlik Replicate, when handling data errors, there are specific actions that can be configured to manage such errors. Based on the documentation, the available options for handling data errors include:

C. Log record to a specific table: This option allows for the logging of error records to a designated table for further analysis and troubleshooting1.

E. Log record to the exceptions table: This is the default action for data errors in Qlik Replicate, where the error record is written to the exceptions table, allowing the task to continue while preserving information about the error1.

The other options listed are not directly related to the actions available for data errors in Qlik Replicate:

A. Reload task and Reload table: These actions are more related to resolving issues at the task or table level, rather than handling individual data errors.

B. Update missing target error on target side: This option does not correspond to a standard data error handling action in Qlik Replicate.

D. Suspend table: While suspending a table is an action that can be taken in response to data errors, it is typically used to halt replication for the affected table until the issue is resolved1.

For a detailed understanding of data error handling in Qlik Replicate, you can refer to the official Qlik Replicate Help documentation, which outlines the various error handling strategies and configurations that can be applied to tasks1.

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.

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.

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

ï‚·Thestrftimefunction is used to format date and time values.

ï‚·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').

ï‚·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.

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.

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.

When the “Apply batched changes to multiple tables concurrently” option is enabled in a Qlik Replicate task, theattrep_apply_exceptioncontrol table stores specific information related to change processing errors. The details stored in this table include:

TASK_NAME: The name of the Qlik Replicate task.

TABLE_NAME: The name of the table.

ERROR_TIME (in UTC): The time the exception (error) occurred.

STATEMENT: The statement that was being executed when the error occurred.

ERROR: The actual error message1.

This information is crucial for troubleshooting and resolving issues that may arise during the replication process.The data in theattrep_apply_exceptiontable is never deleted, ensuring a persistent record of all exceptions1.

The other options do not accurately reflect the information stored in theattrep_apply_exceptioncontrol table:

AandBmention “Warning_Time,” which is not a column in the table.

Dis incorrect because the table does store information about errors.

For more detailed information on theattrep_apply_exceptioncontrol table and its role in handling change processing errors, you can refer to the official Qlik Replicate documentation1.

In Qlik Replicate, notifications can be set up for various events related to tasks, endpoints, and the server itself. The types of notifications that can be configured include:

Task Events: Notifications for task-based events, such as when a task starts or stops, when latency is too high, or when specific errors occur1.

Endpoint Events: While the search results did not explicitly mention “Endpoint Events,” it is common in replication tools to have notifications related to endpoints, such as connectivity issues or endpoint-specific errors.

Server Events: Notifications related to the server, which could include system state changes, performance issues, or server-level errors2.

Therefore, the correct answer isC. Server Events, Endpoint Events, and Task Events, as Qlik Replicate allows setting up notifications for all these types of events. This ensures that administrators are alerted to a wide range of potential issues, allowing for prompt response and resolution.

For detailed instructions on how to set up each type of notification in Qlik Replicate, you can refer to the official Qlik documentation, which provides step-by-step guidance on defining and managing notifications for task events, server events, and other significant occurrences within the replication environment132.