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After a recent deployment, a critical data pipeline in Databricks starts showing unexpected behavior, affecting downstream analytics. What is the best strategy to quickly revert the deployment to its previous stable state?
A
Implement a blue/green deployment strategy in Databricks, allowing quick switch-over to a pre-deployment state if issues are detected.
B
Rely on Databricks workspace snapshots for quick restoration to a point-in-time before the deployment.
C
Manually revert changes by redeploying the previous version of the notebook from a backup.
D
Utilize the Databricks REST API to programmatically revert the deployed notebooks to a previous commit known to be stable.
