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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You've created a new model ref_country_codes. It references a raw table in your data warehouse raw_web_events. To ensure this dependency is valid and up-to-date, you should include which element in your model definition?

A) A custom macro to validate data in raw web events
B) A source freshness test on raw web events
C) A schema test on ref_country_codes
D) A dbt seed file populating ref_country_codes

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

3. A complex transformation in your dbt project pulls data from an external API. Due to rate limits, this API endpoint cannot be queried frequently in production. How could you handle development and testing of this transformation effectively?

A) All of the above could be part of a strategy.
B) Utilize dbt snapshots to capture a static set of API data for use during development and testing.
C) Use environment variables to control API query frequency or switch between live and test data sources.
D) Set up a development environment that utilizes a mock API to simulate external data.

4. Collaborative Fix

A) Add clear commit messages that concisely summarize the purpose of the fix.
B) Include updated model documentation (dbt docs) outlining the logic and reasons for adjustment.
C) Open a pull request with detailed comments explaining the rationale for the changes and any potential side-effects.
D) All of the above.

5. You're merging changes from a feature branch into your main branch. Which of the following Git commands or strategies help keep a clean and easy-to-follow project history?

A) Using git merge to create a merge commit.
B) Utilizing git rebase main before merging to linearize the history.
C) All of the above can be valid strategies, each with different trade-offs.
D) Employing git squash merge for condensing feature branch commits.

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