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After deploying a new version of a data processing workflow in Azure Databricks, critical issues affecting data quality are discovered. What is the most efficient method to quickly rollback the deployment to a stable version?
A
Setting up a custom monitoring solution using Azure Log Analytics to track and alert on individual tenant resource usage, applying scaling adjustments with Azure Automation
B
Manually restoring the previous version of the notebooks from a backup copy stored in Azure Blob Storage
C
Using Azure DevOps release pipelines with artifact versioning to automate and resource usage, to enforce fair use policies through Azure Function automation
D
Configuring Databricks Unity Catalog for data governance, and using its audit logs to monitor and manage resource allocation across tenants with Azure Policy enforcement
