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?

Real Exam

Utilizing Azure Monitor alerts to notify developers of any performance degradation post-deployment, without conducting pre-deployment testing

7.4%

Creating a comprehensive set of test cases that automatically execute post-deployment via Azure Pipelines, comparing actual outputs with expected results

85.1%

Depending entirely on manual testing by developers prior to each deployment to identify any regressions

1.1%

Deploying a custom logging mechanism to monitor performance trends over time, with regressions identified through manual analysis of logs

6.4%

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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?