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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're working in a highly regulated environment. Merges into the main branch require multiple approvals. Which additional steps might need to be incorporated into your merge process?

A) Utilize signed commits to add a layer of traceability and verification-
B) All of the above
C) Link pull requests to detailed change documentation and impact analysis.
D) Use a protected branch workflow on the remote repository with required reviews.

2. (Multiple Select)

A) Ensuring column ordering matches an external specification.
B) Checking for referential integrity with a separate lookup table.
C) Enforcing specific ratios or relationships between aggregated values.
D) Validating that the row count of the staging model falls within an expected range.

3. You need to regularly run performance tests to benchmark a time-sensitive incremental model. These tests involve more complex setup than standard dbt tests. How could you efficiently integrate them into your workflow?

A) Create a dbt operation (macro) to encapsulate the test logic and invoke it as needed.
B) Write the tests as generic tests and use dbt's built-in warning thresholds.
C) Convert them into dbt seeds to easily execute them alongside model runs-
D) Define the performance tests as their own dbt project, triggered separately-

4. You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?

A) Disabling all tests that rely on creating new tables, to avoid impacting the production database.
B) Including smoke tests that run in production to verify core functionality after a deployment.
C) Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
D) Emphasizing tests with longer run times for thorough checks before deployment.

5. (Multiple Select)

A) Consider common reporting needs when designing downstream models.
B) Minimize the number of intermediate models, even if it means complex SQL within a few large models.
C) Materialize all models as 'views' for maximum flexibility and to avoid potential storage bottlenecks.
D) Introduce CTEs within models to create logical groupings of transformations.

Solutions: