Databricks Machine Learning Professional Question 94
Select 3A data science team is implementing a CI/CD pipeline for their machine learning workflows in Databricks. They want to ensure that their ML models perform as expected during deployment and that data-related issues are caught early in the pipeline. Which of the following describe the role of automated testing in this scenario?
- A
Ensures that data transformations produce expected outputs during preprocessing steps.
- B
Validates that model performance metrics meet predefined thresholds after training.
- C
Detects and resolves runtime errors in the production environment after deployment.
- D
Checks that code changes do not break existing ML pipeline functionalities.
- E
Guarantees that the model will perform equally well on unseen production data.
Show answer and explanation
Correct answers: A, B, D
Explanation
Automated testing in ML CI/CD pipelines is crucial for validating data preprocessing, ensuring model quality, and maintaining pipeline stability when introducing code changes. It does not handle runtime production errors or guarantee model performance on unseen data, as those aspects require monitoring and robust model training processes.
- A. Correct.
Correct: Automated testing includes validating that preprocessing steps produce the expected outputs to catch data-related issues early in the pipeline.
- B. Correct.
Correct: Automated tests can verify that a model achieves predefined performance metrics (e.g., accuracy, precision) to ensure quality before deployment.
- C. Incorrect.
Incorrect: Detecting runtime errors in production is typically handled by monitoring rather than automated testing in the CI/CD pipeline.
- D. Correct.
Correct: Automated testing ensures that code changes do not break existing functionalities, which is critical in maintaining pipeline stability.
- E. Incorrect.
Incorrect: Automated testing cannot guarantee model performance on unseen production data since it cannot predict all future data scenarios.