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Updated: Jun 07, 2026
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dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You've merged a pull request but later discover it introduced a regression (unexpectedly broke existing functionality). What's the most common initial step to address this?
A) Open a new pull request with a fix for the regression.
B) Run git revert on the merge commit to undo the changes.
C) Coordinate with the original author of the merged pull request.
D) Use git blame or git log to pinpoint the commit that caused the issue.
2. Which of the following scenarios would likely require you to use the -no-compile flag with the dbt docs generate command?
A) Updating existing column descriptions to reflect recent upstream changes.
B) You've made syntax errors in your model definitions and want to prevent documentation errors due to compilation failures.
C) Adding a new model and its corresponding description in your project.
D) Running the documentation generation process in a production setting where compilation could introduce risks.
3. During a code review, a collaborator suggests modifying a small portion of code you had already committed to your feature branch. What's the most efficient way to address this without creating a completely new commit?
A) Use git revert to undo your previous commit, make the change, and then re-commit.
B) Use git commit -amend to modify your most recent commit.
C) Create a new branch extending from your feature branch, make the change, and open a separate pull request.
D) Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.
4. (Multiple Select)
A) Consider a CI/CD process that deploys new model changes to a pre-production environment for validation first.
B) Run dbt tests automatically as part of the job.
C) Enable email notifications on both job success and failure.
D) Schedule the job to run less frequently to avoid overloading the data warehouse.
5. You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?
A) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
B) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
C) Add a depends_on relationship pointing to the potentially late table within your model.
D) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
Solutions:
| Question # 1 Answer: C,D | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: A,B,C | Question # 5 Answer: B,D |
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