Ultimate access to all questions.
Upgrade Now 🚀
Sign in to unlock AI tutor
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?
A
Configure a manual rollback process that involves restoring the previous version of the job from a backup in Azure Blob Storage.
B
Use Azure DevOps Pipelines with deployment gates that automatically rollback the deployment if data quality checks fail.
C
Leverage Databricks Repos to manage code versions, automatically reverting to the previous commit if the deployed version fails.
D
Implement an automated monitoring system that triggers a rollback in Azure DevOps if predefined data quality or performance metrics are not met post-deployment.
