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How can you automate testing to ensure the effectiveness and accuracy of critical data quality checks and anomaly detection in your Azure Databricks data pipelines?
A
Integrating with Azure Monitor and Azure Log Analytics to create alerts based on metrics that would indicate failures in data quality checks or anomaly detection
B
Developing a suite of Databricks notebooks that generate synthetic data with known quality issues and anomalies, verifying the pipeline‘s ability to detect and flag these
C
Using Azure Data Factory to orchestrate data movement and trigger Databricks jobs, which include data quality tests, before and after deploying updates
D
Leveraging MLflow within Azure Databricks to version control data quality and anomaly detection models, running automated tests against a validation dataset