You are about to deploy a major update to a data transformation pipeline in Azure Databricks. What is the best strategy to ensure the updated pipeline maintains or enhances data quality before going live?

Real Exam

Use Azure Data Factory to orchestrate a parallel run of both the current and updated pipelines, comparing outputs for discrepancies.

3.2%

Implement unit and integration tests within Databricks notebooks that validate data outputs against a controlled set of test data, integrating these tests into your CI/CD pipeline.

82.5%

Conduct manual data validation by comparing outputs from the updated pipeline against expected results for a sample of test data.

1.6%

Leverage Databricks MLflow to track experiment runs with the new pipeline version, using statistical analysis to ensure data quality metrics meet predefined thresholds.

12.7%

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You are about to deploy a major update to a data transformation pipeline in Azure Databricks. What is the best strategy to ensure the updated pipeline maintains or enhances data quality before going live?