After deploying an update to a data processing job in Azure Databricks, you detect a critical fault affecting data quality. What automated strategy would you implement to quickly rollback deployments in case of detected faults?

Real Exam

Configure a manual rollback process that involves restoring the previous version of the job from a backup in Azure Blob Storage.

4.5%

Use Azure DevOps Pipelines with deployment gates that automatically rollback the deployment if data quality checks fail.

22.7%

Leverage Databricks Repos to manage code versions, automatically reverting to the previous commit if the deployed version fails.

21.2%

Implement an automated monitoring system that triggers a rollback in Azure DevOps if predefined data quality or performance metrics are not met post-deployment.

51.5%

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After deploying an update to a data processing job in Azure Databricks, you detect a critical fault affecting data quality. What automated strategy would you implement to quickly rollback deployments in case of detected faults?