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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need to test for consistency between a raw source table and its corresponding dbt model after transformation. This requires comparing many columns for null counts and matching value distributions. Which approach would be most efficient?

A) Creating a custom dbt test that leverages statistical comparison techniques.
B) Relying entirely on generic not_null and unique tests.
C) Manually writing SQL queries to separately calculate and compare these metrics.
D) Using dbt's built-in source freshness tests for a high-level check.

2. You've created a new model ref_country_codes. It references a raw table in your data warehouse raw_web_events. To ensure this dependency is valid and up-to-date, you should include which element in your model definition?

A) A dbt seed file populating ref_country_codes
B) A source freshness test on raw web events
C) A custom macro to validate data in raw web events
D) A schema test on ref_country_codes

3. You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?

A) Set up a CIICD test that runs the macro in isolation using both database versions.
B) Use database-specific functions to query the version information directly from within dbt models.
C) Add verbose logging within the macro to track execution differences step by step.
D) Temporarily introduce a conditional branch into the macro code that is environment-aware.

4. You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

A) Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
B) Reference an external Markdown file within the source description.
C) Write a longer description directly within the YAML file, breaking it across multiple lines.
D) dbt documentation does not support multi-paragraph descriptions for sources.

5. You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

A) Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
B) Close the original pull request and create a new branch with the revised implementation.
C) Request that your collaborators edit the code directly within the pull request interface.
D) Directly modify the commits on your existing feature branch using git rebase -i.

Solutions: