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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A marketing team needs a model to calculate the 30-day rolling average of new customer signups. The raw signups table is very large and updated hourly. Which approach strikes the best balance between accuracy and computational efficiency?

A) An incremental model that updates the rolling average calculations only for new signup records.
B) A table materialization that completely rebuilds the rolling average daily.
C) An ephemeral model that calculates the rolling average within a series of complex CTEs.
D) A view materialization that recalculates the 30-day rolling average for each query.

2. You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A) Using dbt tags to selectively execute parts of your project.
B) Creating multiple deployment environments in dbt Cloud.
C) dbt profiles defined in your dbt_project.yml file.
D) dbt's -vars flag to override variable values.

3. Your team decides to strictly enforce a linear history on the main branch. How does this decision impact your development and merge workflow?

A) All of the above.
B) Feature branches would likely need to utilize git rebase before merging.
C) Pull requests would always show a single commit upon merging with the main branch.
D) Code reviews might need to focus on the overall change in the pull request instead of individual commits.

4. You're refactoring a project with long model files containing intertwined logic. Which of the following is NOT a typical sign of a good candidate for applying modularity principles?

A) Portions of the logic form distinct conceptual steps in the transformation process.
B) The same SQL patterns are repeated with minor variations.
C) The models are relatively small and perform simple transformations.
D) Renaming code blocks would improve readability.

5. (Multiple Select)

A) Using the 'defer' option to partially run a DAG and expose errors early
B) Implementing a robust suite of schema tests to validate object references-
C) Defining source freshness thresholds to catch issues with missing tables or views-
D) Setting up a linter for your SQL files, either as part of your IDE or code editor

Solutions: