To ensure updates to data transformation logic in your Azure Databricks notebooks do not introduce errors or regressions, which automated testing framework should you implement?

Real Exam

Setting up a continuous integration/continuous deployment (CI/CD) pipeline using GitHub Actions to run tests against a mirrored production environment

24.3%

Integrating Databricks notebooks with Azure DevOps pipelines, using PyTest for regression testing with mock data sets

65.8%

Utilizing Databricks Jobs with scheduled test runs, comparing outputs to expected results stored in Azure Blob Storage

7.2%

Writing custom test scripts within Databricks notebooks that are manually executed before each deployment

2.7%

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To ensure updates to data transformation logic in your Azure Databricks notebooks do not introduce errors or regressions, which automated testing framework should you implement?