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To ensure comprehensive monitoring of your data pipelines in Databricks, tracking both performance metrics and data quality, which combination of tools and techniques would you recommend?
A
Use Databricks' built-in dashboard for performance monitoring and manually review data samples for quality.
B
Configure Azure Monitor with Databricks metrics and logs, and use Data Quality rules within Azure Purview.
C
Leverage MLflow for monitoring job performance and integrate Apache Griffin for data quality checks.
D
Implement custom logging in your Spark jobs to track performance and data quality metrics, storing logs in Delta tables.
