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To ensure the reliability and stability of a complex ETL pipeline in Azure Databricks, what strategy would you adopt for automated regression testing to detect any breaks or performance regressions automatically?
A
Utilizing Azure Monitor alerts to notify developers of any performance degradation post-deployment, without conducting pre-deployment testing
B
Creating a comprehensive set of test cases that automatically execute post-deployment via Azure Pipelines, comparing actual outputs with expected results
C
Depending entirely on manual testing by developers prior to each deployment to identify any regressions
D
Deploying a custom logging mechanism to monitor performance trends over time, with regressions identified through manual analysis of logs
