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?

Real Exam

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

16.1%

Manually restoring the previous version of the notebooks from a backup copy stored in Azure Blob Storage

39.0%

Using Azure DevOps release pipelines with artifact versioning to automate and resource usage, to enforce fair use policies through Azure Function automation

30.5%

Configuring Databricks Unity Catalog for data governance, and using its audit logs to monitor and manage resource allocation across tenants with Azure Policy enforcement

14.4%

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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?