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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A) Your tests were correct, but a bug in dbt prevented them from running successfully.
B) Critical assumptions were made about the data in the Bl layer, independent of dbt models.
C) There are logic errors in your dbt models that the tests didn't catch.
D) The tests you wrote were too strict, causing false positives.

2. A colleague expresses confusion about how dbt uses the dbt_project.yml file. Which of the following is the MOST accurate high-level explanation?

A) The dbt_project yml file contains the actual SQL code that defines your data transformations.
B) The dbt_project. yml file serves as a temporary cache for in-development models.
C) The dbt_projectyml file acts as a blueprint for how dbt should generate your data models, tests, and documentation.
D) The dbt_project.yml file is primarily used for version control and collaboration in dbt projects

3. A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?

A) Set up an alert to notify users when the upstream data source is not refreshed on time.
B) Utilize the 'defer' option to allow upstream models to run while awaiting the source update.
C) Employ a snapshot of the third-party source to create a historical record.
D) Configure a 'view' that gracefully handles missing data from the third-party source.

4. You need to create a model that combines data from a large fact table with smaller dimension tables. Performance is paramount, and the data in the fact table updates incrementally but frequently. Which materialization strategy is likely to provide the optimal balance of efficiency and freshness?

A) Materialize all tables (fact and dimensions) as views.
B) Materialize the fact table as a table and the dimension tables as incremental models.
C) Materialize all tables (fact and dimensions) as tables.
D) Materialize the fact table as an incremental model and the dimension tables as views.

5. 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 commit -amend to modify your most recent commit.
B) Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.
C) Create a new branch extending from your feature branch, make the change, and open a separate pull request.
D) Use git revert to undo your previous commit, make the change, and then re-commit.

Solutions: