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Answer: Diagnose the schema mismatch using Azure Data Factory's monitoring tools, correct the schema in the source data, and use the pipeline's retry mechanism to reload the data.
Diagnosing the issue using Azure Data Factory's monitoring tools allows for precise identification of the schema mismatch. Correcting the schema in the source and utilizing the pipeline's retry mechanism ensures that the data load can resume from the point of failure without the need to re-run the entire pipeline, thus saving time and resources.
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Consider a scenario where a batch load of customer data into Azure SQL Database fails due to a schema mismatch. Outline the steps you would take to handle this failure, including how you would diagnose the issue, rectify the schema mismatch, and ensure the data load resumes without data loss.
A
Delete the failed batch and re-run the entire pipeline from scratch.
B
Diagnose the schema mismatch using Azure Data Factory's monitoring tools, correct the schema in the source data, and use the pipeline's retry mechanism to reload the data.
C
Manually update the schema in Azure SQL Database to match the source data and re-run the batch.
D
Ignore the failure and proceed with the next batch.