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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:

A) Use detailed commit messages to explain the rationale behind each set of related changes.
B) Provide a comprehensive summary of the intent and scope of the changes within the pull request description.
C) Resubmit the pull request as several smaller, more focused pull requests.
D) All of the above.

2. You discover a flaw in a data cleaning process applied early in your raw data transformation. What implications does this have for the rest of your dbt pipeline and the distinction between production vs. development?

A) It highlights the importance of testing and validation at each transformation stage, not just in the final reporting layer.
B) You might need to re-run models downstream of the fix across both development and production to ensure data integrity.
C) All of the above.
D) There's a risk that incorrect data exists in both development and production, leading to unreliable modeling.

3. During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?

A) Temporarily decrease the sample size of data during development, while reverting to full data processing in other environments.
B) Strategically employ ephemeral models in development to avoid materializing intermediate data repeatedly.
C) All of the above.
D) Use development environment settings that incrementally build models only when code changes.

4. (Multiple Select)

A) Tables intended to be empty as part of a process truly have zero records.
B) Foreign key relationships are consistently enforced.
C) Columns intended to contain timestamps actually store valid timestamp values.

5. You've defined development, staging, and production targets in your dbt project. You want to prevent accidental execution of destructive model materialization strategies (like drop or truncate) on the production target. How could you achieve this?

A) Implement a CI/CD process that includes checks to prevent specific dbt commands from running in the production environment
B) Create a separate profiles.yml file for production that disables dangerous materialization strategies.
C) Write a custom macro that only executes such operations on non-production targets.
D) Rely purely on thorough code reviews to prevent execution of these commands in production.

Solutions: