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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

A user reports a critical security issue discovered in a third-party dbt package you use. Where could you most likely find updated information about potential vulnerabilities or fixes?

A. Directly from the maintainer of the dbt package, often in a GitHub repository or similar
B. The locally generated dbt documentation for the installed packages.
C. The packages.yml file in your dbt project.
D. Within dbt Cloud's central package documentation and release notes.

Question 2

Which of the following are essential elements of a well-structured pull request description?

A. Detailed logs of each Git command used during development.
B. Explanations of the reasoning behind major changes and implementation choices.
C. References to related issues in the project tracker, if applicable.
D. A concise title summarizing the overall changes.

Question 3

You introduce a set of schema tests on source tables. These tests run successfully, but upon investigation, you find subtle data quality issues are still being missed. What are potential reasons for this?

A. Schema tests only check basic structure and not the specific content of the data.
B. There's a bug in dbt's schema test implementation.
C. Schema tests shouldn't be relied on for comprehensive data quality assurance.
D. Schema tests only run once per day by default.

Question 4

You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Contact the repository hosting service for assistance.
B. Permanently remove the sensitive file from Git history using tools like git filter-branch.
C. Both B and C.
D. Use git reset -hard to revert the commit and remove the file locally.

Question 5

You're building tests for a dbt model that performs a join across multiple large datasets. You notice performance issues - tests are taking a very long time to run. What might be optimization strategies?

A. Create snapshots of the source tables and test on those snapshots instead.
B. Refactor model SQL to use incremental logic to avoid full table scans.
C. Accept that tests on complex models will always be slow.
D. Focus on tests that can validate a subset of the data rather than the entire dataset.

Solutions: