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Answer: Write a script that uses the Databricks CLI to submit jobs and validate their outcomes against expected results.
Automating testing for data transformation pipelines in Databricks requires a systematic approach to ensure the accuracy and reliability of the pipelines. Writing a script that utilizes the Databricks CLI to submit jobs and validate their outcomes against expected results is the most suitable method for integrating pipeline testing into the CI/CD workflow. This approach ensures tests are consistently executed in a repeatable manner, validates pipeline outcomes against expected results for error detection, integrates seamlessly into the CI/CD workflow for thorough testing before production deployment, and offers scalability and reusability across multiple pipelines.
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Your team is looking to automate the testing of data transformation pipelines developed in Databricks as part of your CI/CD workflow. Which approach using the Databricks CLI would best achieve this?
A
Configure the CLI to send notifications to the team upon job completion, regardless of success or failure.
B
Write a script that uses the Databricks CLI to submit jobs and validate their outcomes against expected results.
C
Use the CLI to export notebooks and run them locally in a simulated environment.
D
Manually trigger tests using the Databricks CLI for each pipeline deployment.