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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 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.

Question 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.

Question 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-

Question 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.

Question 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: