Databricks Data Engineer Professional Question 255
Select 3You are tasked with deploying a production-grade data pipeline in Databricks that processes incoming data every hour. Before deploying, you want to ensure the pipeline works as expected and can handle edge cases. Which of the following steps should you include in your testing strategy to ensure a robust deployment?
- A
Perform unit tests to validate individual transformations in the pipeline.
- B
Run the pipeline in production mode without testing to confirm real-world behavior.
- C
Execute integration tests to ensure the end-to-end functionality of the pipeline.
- D
Simulate failure scenarios to test error handling and alerting mechanisms.
- E
Manually inspect the output of the pipeline for every run to check for anomalies.
Show answer and explanation
Correct answers: A, C, D
Explanation
A robust testing strategy for a production-grade data pipeline should include unit tests to validate individual transformations, integration tests to confirm end-to-end functionality, and failure simulations to ensure proper error handling and alerting mechanisms. These steps ensure the pipeline is reliable, scalable, and resilient before deployment. Manually inspecting outputs or skipping tests altogether is not aligned with professional best practices.
- A. Correct.
Unit tests are critical for validating the correctness of individual transformations and logic in the pipeline. This ensures that each component behaves as expected in isolation.
- B. Incorrect.
Deploying the pipeline in production without testing is risky and does not align with best practices. It could lead to service interruptions or data quality issues.
- C. Correct.
Integration tests are necessary to ensure that the pipeline functions correctly as a whole and all components work seamlessly together.
- D. Correct.
Simulating failure scenarios helps verify that the pipeline can gracefully handle issues, such as data corruption or system failures, and that alerting mechanisms work as intended.
- E. Incorrect.
Manually inspecting the output for every run is not a scalable or practical approach for testing. Automated testing and monitoring should replace manual checks.