Pass Using QREP Exam Dumps

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 topic1.

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 resources21.

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.

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.

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.