Databricks Data Engineer Professional Question 258
Select 3You are working on deploying a data pipeline in your Databricks workspace. As part of the deployment process, you want to ensure the pipeline behaves as expected after deployment. Which of the following steps are most appropriate for testing and validating the deployment of the pipeline?
- A
Run unit tests in a staging environment to validate individual functions in the pipeline.
- B
Manually inspect the logs of the pipeline in the production environment without running any tests.
- C
Perform integration tests in a staging environment to validate how pipeline components work together.
- D
Use a canary deployment strategy to validate the pipeline with a small subset of production data.
- E
Directly deploy the pipeline to production without any testing, as Databricks automatically validates pipelines during deployment.
Show answer and explanation
Correct answers: A, C, D
Explanation
Testing and validating pipelines before and during deployment is critical to ensure they function as expected. Running unit and integration tests in a staging environment helps identify issues early. Additionally, using a canary deployment strategy allows you to test the pipeline with minimal risk in a production-like environment. These steps together form a robust testing and deployment strategy in Databricks.
- A. Correct.
Unit tests are a critical step in validating the correctness of individual pipeline components in an isolated environment, ensuring they function as expected before full deployment.
- B. Incorrect.
Manually inspecting logs without running tests is not a reliable or scalable method for validating a pipeline's behavior after deployment.
- C. Correct.
Integration tests in a staging environment are essential to confirm that all pipeline components interact correctly and generate the expected outputs.
- D. Correct.
Canary deployment is a best practice for testing with a small subset of production data, minimizing risk while validating the pipeline in a live environment.
- E. Incorrect.
Directly deploying to production without testing is not recommended, as Databricks does not automatically validate the correctness of your pipeline logic during deployment.