Question 80

Your team is building a CI/CD process for a Lakeflow project deployed with Databricks Asset Bundles. You are defining the automated testing strategy and want to map each test type to the right tool and scope so that the pipeline catches defects at the cheapest possible layer before promotion to production. The team agrees on these layers: - **Unit tests** — validate isolated transformation logic (a single function or table) with mock data, fast, no production data. - **Integration tests** — validate that workflows and data pipelines run end-to-end against real Databricks compute. Which TWO statements correctly describe Databricks-recommended testing practices for these layers? (Choose TWO.)

• AUnit tests for Python business logic should be implemented with a framework such as `pytest`, and for Lakeflow Spark Declarative Pipelines you can write Python unit tests that run a subset of the pipeline against a fully isolated catalog using mock data and validate results with standard `pytest` assertions.
• BIntegration tests for workflows and data pipelines should be run after deployment, for example using the Databricks CLI `databricks bundle run` to execute the deployed job/pipeline against real compute, and tools such as `chispa` can validate Spark DataFrames.
• CUnit tests must always be executed against the production Unity Catalog so that they exercise the exact data the job will see at run time.
• D`databricks bundle validate` replaces the need for unit and integration tests because it confirms the business logic of every transformation is correct.
• EIntegration tests cannot be automated in CI/CD and must be performed manually by a reviewer in the workspace UI before each release.