To add a new column to a single table in a Qlik Replicate task, the correct path is through Table Settings. Here's the process you would typically follow:

Navigate to the Table Settings of the table you wish to modify within your task.

Go to the General section.

Use the option to Add New Column.

This process allows you to add a column directly to the table's schema as part of the task configuration. It's important to note that this action is part of the task's design phase, where you can specify the schema changes that should be applied to the data as it is replicated.

The other options listed, such as New Transformation or Select Table -> Transform, are not the direct paths for adding a new column to a table's schema within a task. They are related to different aspects of task configuration and transformation1.

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.

The ODBC drivers in Qlik Replicate are located in the \Replicate\bin directory. This is supported by the documentation from Qlik, which indicates that when installing required clients such as the Microsoft ODBC Driver for SQL Server, the working directory should be changed to /bin1. Additionally, for other prerequisites involving driver locations, the instructions also refer to changing the working directory to <Replicate-Install-Dir>/bin23. This consistent reference to the \bin directory across different parts of the Qlik Replicate documentation verifies that the ODBC drivers are indeed located in the \Replicate\bin directory.

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

When handling data errors in Qlik Replicate, especially when data might be truncated, maintaining full table integrity is crucial. The best approach to handle this situation is to log the record to the exceptions table. Here's why:

Log record to the exceptions table (D): This option allows the task to continue processing while ensuring that any records that could not be applied due to errors, such as truncation, are captured for review and resolution. The exceptions table serves as a repository for such records, allowing administrators to address the issues without losing the integrity of the full dataset1.

Stop Task (A): While stopping the task will prevent further data processing, it does not provide a mechanism to handle the specific records that caused the error.

Suspend Table (B): Suspending the table will halt processing for that specific table, but again, it does not address the individual records that may be causing truncation issues.

Ignore Record : Ignoring the record would mean that the truncated data is not processed, potentially leading to data loss and compromising table integrity.

Therefore, the verified answer is D. Log record to the exceptions table, as it allows for the identification and resolution of specific data errors while preserving the integrity of the overall table data12.