IICS CDI Quiz 2

The feature of Cloud Mapping Designer used to view data for individual transformations to test the mapping logic is called Data Preview.  Data Preview is a crucial feature in Cloud Mapping Designer that allows developers to view sample data for individual transformations within a mapping. It facilitates the testing and validation of the mapping logic at various stages of development.

An active transformation can change the number of rows that pass through the transformation. For example, the Filter transformation is active because it removes rows that do not meet the filter condition.
A passive transformation does not change the number of rows that pass through the transformation.
You cannot connect multiple active transformations or an active and a passive transformation to the same downstream transformation or transformation input group. You might not be able to concatenate the rows. An active transformation changes the number of rows, so it might not match the number of rows from another transformation.

The transformation which can change or alter the number of records is known as ‘Active Transformation‘. It means that the number of records or rows going to the transformation is not equal to the number of records coming out of the transformation. Active transformation can act as passive transformation.
For example: Filter transformation is active transformation. However, it can be converted into passive transformation by making the filter condition always ‘TRUE’ or ‘1’.

When you use multiple partitions, the mapping task divides data into partitions and processes the partitions concurrently, which can optimize performance. Enable partitioning when you configure the Source transformation in the Mapping Designer. When you configure partitions in the Source transformation, partitioning occurs throughout the mapping.

Error Handling settings allow you to determine if the session fails or continues when it encounters pre-session command errors, stored procedure errors, or a specified number of session errors. In IICS you can use two functions for handling user defined errors: error() and abort().
1) error()
The error function makes the integration service skip the row and display the error message that was defined by the user.
2) abort()The abort function makes the integration service stop the workflow run and display the error message that was defined by the user. Here we are stopping the mapping run itself without proceeding further.
The second exception handling technique for user defined errors is error tables: when a task fails, errors are logged into the error table. When a task fails, see the error tables for more information about the errors.

In replication task you can choose to replicate both single and multiple objects based on your requirements.

In Informatica Intelligent Cloud Services (IICS), $LastRunTime is the last date and time in GMT time zone when the task ran successfully and is always stored in GMT timezone.

If the task fails with an error, then $lastruntime value would not be updated instead it would continue to use the value from the previous success run.

A Replication Task in Informatica Intelligent Cloud Services (IICS) allows you to replicate data from a cloud-based application or a relational database table to a target. It is specifically designed for efficiently copying data from a source to a target system, facilitating data replication and synchronization.

Aggregator transformations often slow performance because they must group data before processing it. Aggregator transformations need additional memory to hold intermediate group results.Use the following guidelines to optimize the performance of an Aggregator transformation:
Group by simple columns.
Use sorted input.
Use incremental aggregation.
Filter data before you aggregate it.
Limit port connections.

Each Mapplet transformation can contain one mapplet. You can add multiple Mapplet transformations to a mapping or mapplet.