Qlik Replicate Certification QREP Exam Practice Test

Answer : D

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

The strftime function 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 the strftime function with the %s format 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 the SAR_H_COMMIT_TIMESTAMP and the Unix epoch start date. Here's a breakdown of the function:

strftime('%s', SAR_H_COMMIT_TIMESTAMP) converts the SAR_H_COMMIT_TIMESTAMP to Unix time.

strftime('%s','1970-01-01 00:00:00') gives the Unix time for the epoch start date, which is 0.

Subtracting the second part from the first part is not necessary in this case because the Unix epoch time is defined as the time since 1970-01-01 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 use datetime instead of strftime, which is not the correct function for this operation1.

Option C incorrectly includes <code>datetime.datetime</code>, which is not a valid function in Qlik Replicate and seems to be a mix of Python code and SQL1.

Option E uses Time.now.strftime, which appears to be Ruby code and is not applicable in the context of Qlik Replicate1.

Therefore, the verified answer is D, as it correctly uses the strftime function to convert a timestamp to Unix time in Qlik Replicate1.

Answer : C

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.

Answer : A, C, F

Qlik Replicate offers several methods for parallel load during a full load process to accelerate the replication of large tables by splitting the table into segments and loading these segments in parallel. The three primary ways Qlik Replicate allows parallel loading are:

Use Data Ranges:

This method involves defining segment boundaries based on data ranges within the columns. You can select segment columns and then specify the data ranges to define how the table should be segmented and loaded in parallel.

Use Partitions - Use all partitions - Use main/sub-partitions:

For tables that are already partitioned, you can choose to load all partitions or use main/sub-partitions to parallelize the data load process. This method ensures that the load is divided based on the existing partitions in the source database.

Use Partitions - Specify partitions/sub-partitions:

This method allows you to specify exactly which partitions or sub-partitions to use for the parallel load. This provides greater control over how the data is segmented and loaded, allowing for optimization based on the specific partitioning scheme of the source table.

These methods are designed to enhance the performance and efficiency of the full load process by leveraging the structure of the source data to enable parallel processing

Answer : A, C

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.

Answer : D

In the Qlik Enterprise Manager Analytics GUI, users can view messages related to both server and task trends. This includes:

Server-specific trends: These are metrics and trends related to the performance and usage of the servers, such as memory consumption and disk usage1.

Task-specific trends: These include metrics and trends specific to individual tasks, such as the number of tables and records processed, throughput, and the number of changes applied1.

Therefore, the correct answer is D. Server and task trends, as the Analytics tab in Qlik Enterprise Manager allows users to review trends over a specific time period for both servers and tasks. Users can filter the data to show information for a particular timeframe and for particular tasks, Replicate servers, source databases, and target databases2.

For more detailed information on the types of messages and trends that can be viewed in the Qlik Enterprise Manager Analytics GUI, you can refer to the official Qlik documentation on Analytics dashboards and Analytics.

Answer : D

When using LogStream staging for replication in Qlik Replicate, the Staging folder is compressed and stored on the Qlik Replicate server itself. Here's the process:

A Log Stream Staging task is defined to replicate changes from the source endpoint to the Log Stream Staging folder1.

This task creates a ''Staging'' file in the Log Stream Staging folder, which contains the changes from the source database transaction log as a compressed binary file1.

The storage path for the streamed data, which includes the Staging folder, must be specified in the Log Stream target endpoint settings, and it is explicitly stated that this path should be located on the Replicate Server machine2.

Therefore, the correct answer is D. On the Qlik Replicate server, as the staging folder is located on the machine where Qlik Replicate server is running, and it is where the compressed staging files are stored12.

Answer : C

To run a Qlik Replicate Task at a high-performance level, the main hardware components that are recommended include:

Cores: A higher number of cores is beneficial for handling many tasks running in parallel and for prioritizing full-load performance1.

SSD (Solid State Drive): SSDs are recommended for optimal performance, especially when using a file-based target or dealing with long-running source transactions that may not fit into memory1.

Network bandwidth: Adequate network bandwidth is crucial to handle the data transfer requirements, with 1 Gbps for basic systems and 10 Gbps for larger systems being recommended1.

The other options do not encompass all the recommended hardware components for high-performance levels in Qlik Replicate tasks:

A . SSD, RAM: While these are important, they do not include the network bandwidth component.

B . Cores, RAM: This option omits the SSD, which is important for disk performance.

D . RAM, Network bandwidth: This option leaves out the cores, which are essential for processing power.

For detailed hardware recommendations for different scales of Qlik Replicate systems, you can refer to the official Qlik documentation on Recommended Hardware Configuration.