Databricks Certified Data Engineer - Professional
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Databricks CI/CD Workflows
A data engineer has created a pipeline that implements unit tests and has run this pipeline after pushing changes to the repo. Which of the following steps does the data engineer need to take in order to create a complete Workflow? Select one response.
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A
They need to anonymize any sensitive information in their tables.
B
They need to run the implementation test when code is moved to a staging environment.
C
They need to create data quality constraints to prevent bad records from entering the production table.
D
They need to modularize their code to make it more efficient and readable.
Explanation:
Explanation
In a complete CI/CD (Continuous Integration/Continuous Deployment) workflow, after implementing unit tests and running them after pushing changes to the repository, the next critical step is to run implementation tests when code is moved to a staging environment.
Why option B is correct:
- Staging environment testing is a key component of CI/CD workflows where code is tested in an environment that closely resembles production
- This ensures that the pipeline works correctly before deploying to production
- Implementation tests in staging validate that the integration between different components works as expected
Why other options are incorrect:
- Option A (anonymize sensitive information): While important for data privacy, this is not specifically part of creating a complete CI/CD workflow
- Option C (create data quality constraints): Data quality is important but represents a different aspect of data engineering, not specifically the workflow creation process
- Option D (modularize code): Code organization is a development best practice but not a required step for completing a CI/CD workflow
The complete CI/CD workflow typically includes: development → unit testing → staging environment testing → production deployment.
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