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Answer: Enable detailed logging and use exception handling blocks.
Enabling detailed logging and using exception handling blocks in Azure Data Factory ensures that any errors during the transformation process are captured and logged. This approach allows for quick identification and resolution of issues, preventing data loss or corruption and ensuring the smooth operation of the pipeline.
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You are configuring error handling for a data transformation process in Azure Data Factory. What steps would you take to ensure that any errors during the transformation are captured, logged, and handled appropriately to prevent data loss or corruption?
A
Enable detailed logging and use exception handling blocks.
B
Increase the timeout settings for the pipeline.
C
Use a separate error handling pipeline.
D
Disable the pipeline on error detection.