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Answer: Use Azure Data Factory's built-in error handling and retry mechanisms.
Azure Data Factory's built-in error handling and retry mechanisms provide a robust solution for managing exceptions in a data pipeline. This approach ensures that the pipeline can recover from transient errors and continue processing without manual intervention.
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You are configuring exception handling in a batch processing solution that reads from and writes to a Delta Lake. What strategies would you implement to ensure that the pipeline continues to function smoothly even in the presence of errors?
A
Implement try-except blocks around critical operations and log errors for manual review.
B
Use Azure Data Factory's built-in error handling and retry mechanisms.
C
Set up alerts for any exceptions and pause the pipeline until manual intervention.
D
Ignore exceptions and continue processing, assuming that minor errors will not impact overall results.
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