Testing & Quality Assurance Flashcards

In IDMC, which transformation is most commonly used to test conditional routing logic by sending data down different paths based on field values?

Answer: Router

The Router transformation evaluates conditions and routes rows to different output groups, making it the right choice for testing conditional data paths.

What is the effect of enabling 'Verbose Data' tracing level on an IDMC Mapping Task during testing?

Answer: It logs input and output row data for every transformation, greatly increasing log volume

Verbose Data tracing captures actual row data flowing through each transformation, which is invaluable for debugging but produces very large logs.

A QA engineer is testing an IDMC CDC (Change Data Capture) task. Which scenario is most important to validate during testing?

Answer: That inserts, updates, and deletes are all correctly propagated to the target

CDC tasks must correctly handle all three DML change types — inserts, updates, and deletes — to ensure complete change replication.

Which IDMC component allows a QA team to monitor the health and success rate of integration tasks over time through visual dashboards?

Answer: Operational Insights

Operational Insights provides dashboards with historical trend data, success/failure rates, and performance metrics for integration tasks.

During regression testing of an IDMC mapping after a schema change, which action should be performed first?

Answer: Refresh metadata on affected source and target objects

Refreshing metadata pulls the updated schema from the source/target systems so the mapping reflects the new column definitions before testing.

In IDMC, what happens when a mapping task encounters a non-fatal error and the 'Stop on Error' threshold has not been reached?

Answer: The row is written to the error log and processing continues for remaining rows

Non-fatal errors below the Stop on Error threshold cause the row to be logged as an error while the task continues processing subsequent rows.

Which IDMC feature helps QA teams enforce data quality rules by flagging or rejecting records that do not meet defined business standards before loading to the target?

Answer: Data Quality transformation (Standardize, Validate)

Data Quality transformations such as Validate and Standardize evaluate records against business rules and can flag or route non-conforming rows before they reach the target.