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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. After merging a large pull request, you notice a critical issue introduced in one of the commits. What actions might be necessary?

A) Identify the problematic commit using git log or git blame.
B) Use git cherry-pick to apply specific commits from the reverted change to another branch, effectively restoring the necessary parts.
C) All of the above could be needed as part of the fix.
D) Use git revert to create a new commit that specifically undoes the problematic changes.

2. You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A) Enforce a strict character limit on source descriptions-
B) Utilize Markdown within your descriptions to improve readability with headings and line breaks-
C) Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
D) Split your source description into multiple properties (e.g., description_short and description_long)

3. (Multiple Select)

A) Using SELECT * to include all columns dynamically
B) Writing custom macros to encapsulate frequently used SQL logic.
C) Leveraging window functions to calculate values across rows or groups.
D) Nesting multiple correlated subqueries within a single query_

4. (Multiple Select)

A) IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.
B) Write custom macros to log intermediate data for each model.
C) Run dbt docs generate and dbt docs serve to inspect the lineage graph.
D) Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.

5. You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?

A) The accepted_values test configuration has the wrong column specified.
B) A downstream process is overwriting the results after the model is built.
C) Your test case is accidentally permitting null values, which bypass the check.
D) The model's data type isn't compatible with the accepted_values test.

Solutions: