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Answer: Review the error logs in Azure Data Factory, use the Azure Monitor integration to gather additional insights, and iteratively refine the transformation logic based on identified issues.
Reviewing error logs and leveraging Azure Monitor for deeper insights allows for a systematic approach to diagnosing the root cause of the failure. Iteratively refining the transformation logic based on these findings ensures that the issue is addressed accurately and effectively, improving the reliability of the pipeline.
Author: LeetQuiz Editorial Team
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In a scenario where a data pipeline in Azure Data Factory consistently fails during the execution of a complex transformation activity, describe the troubleshooting steps you would take to identify and resolve the issue, including the tools and methods you would use to diagnose the problem.
A
Restart the entire pipeline and hope the issue resolves itself.
B
Review the error logs in Azure Data Factory, use the Azure Monitor integration to gather additional insights, and iteratively refine the transformation logic based on identified issues.
C
Ignore the failed activity and proceed with the next activities in the pipeline.
D
Manually execute the transformation activity outside of the pipeline to see if it works.
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