After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

• A.There are logic errors in your dbt models that the tests didn't catch.
• B.Critical assumptions were made about the data in the Bl layer, independent of dbt models.
• C.The tests you wrote were too strict, causing false positives.
• D.Your tests were correct, but a bug in dbt prevented them from running successfully.

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Your dbt project contains both source descriptions (within your sources.yml file) and model descriptions. You notice that the source descriptions don't appear in the generated docs alongside the relevant models. What's then most likely reason?

• A.Your dbt_projectyml file is lacking the proper configuration to include source descriptions.
• B.Source descriptions only appear when you navigate directly to the Sources section of the documentation_
• C.Source tables need to be explicitly tagged within your model configuration for their descriptions to appear
• D.A recent update to dbt has changed how source descriptions are integrated into the documentation.

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You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

• A.Your upstream ETL process has a logic error, leading to incorrect data.
• B.The baseline dataset itself has become corrupted
• C.There are differences in data types or time zone handling between the model and baseline.
• D.Your tests are too strict and need more generous tolerances.

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You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?

• A.Your macro implementation has a subtle logic error.
• B.The documentation generator is caching the old version of the macro.
• C.Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.
• D.The macro might only be applied to specific model types.

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Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?

• A.Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
• B.Run dbt test multiple times with varying filters based on desired severity levels.
• C.Rely on your data observability platform to differentiate and alert based on dbt test results.
• D.Use dbt tags for classifying tests, then selectively execute them.

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