Databricks Certified Data Engineer - Professional
Get started today
Ultimate access to all questions.
Ultimate access to all questions.
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?
Explanation:
Option D is the most suitable proposition for implementing an automated strategy to quickly rollback deployments in case of detected faults. Here's why:
Automated Monitoring System: By implementing an automated monitoring system, you can continuously monitor the data quality and performance metrics of the deployed job in real-time. This allows for quick detection of any faults post-deployment.
Predefined Metrics: Setting predefined data quality or performance metrics ensures that the decision to rollback is based on objective criteria, not subjective judgment.
Integration with Azure DevOps: Integrating the monitoring system with Azure DevOps enables automatic triggers for rollback if metrics are not met, eliminating the need for manual intervention.
Quick Response Time: An automated system ensures real-time response to faults, minimizing impact on data quality and performance, thus maintaining the integrity of your data processing job.
This approach is efficient and effective for addressing faults in deployments and ensuring data quality in Azure Databricks.