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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. While onboarding new data sources to your dbt project, you discover some have unexpectedly high null rates in key columns. How would you integrate tests for this into your dbt workflow?

A) Include a data quality monitoring step in your upstream ETL process before data reaches dbt
B) Write tests within the initial staging models that calculate and check the null percentage.
C) Create not_null tests on the raw source tables in a separate dbt project dedicated to source validation.
D) Designate some nulls as acceptable by extending the not_null test with the OR condition.

2. You want to host your dbt project documentation on an internal company server that's behind a firewall. Which approach would be best suited for this scenario?

A) Utilize the dbt docs serve command, making the local web server accessible within your private network.
B) Deploy the docs using dbt Cloud's built-in hosting features.
C) Directly modify the HTML and CSS of the generated documentation site to add firewall-specific rules.
D) Upload the static files generated by dbt docs generate to your internal web server.

3. You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?

A) Directly in the manifest.json or packages.yml files within your project
B) The GitHub repository or documentation of the dbt package.
C) Only the authors of the dbt package would be able to address the vulnerability.
D) Centralized dbt documentation or a discussion forum.

4. When merging code on a remote repository hosting platform (like GitHub), you notice options like "squash and merge" and "rebase and merge." Which factors would influence your choice between these options?

A) Your team's established conventions and preferences for maintaining the project history.
B) All of the above.
C) The size and complexity of the changes within the pull request.
D) Whether you want to preserve the feature branch commit history in detail.

5. In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A) Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
B) When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
C) In environments where strict data governance and transformation processes are in place.
D) When performance is a top priority, and the overhead of additional data transformations is undesirable.

Solutions: