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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You've meticulously added descriptions and tests to your dbt models. However, after running dbt docs generate, the tests don't appear in the generated documentation. Which of the following reasons are most likely?

A) You are using a custom schema test macro that isnt configured to be included in the documentation.
B) Your tests have incorrect syntax, and dbt is silently skipping them during documentation generation.
C) The dbt_project.yml file may not have the appropriate settings for including tests in the documentation.
D) There is a bug in the version of dbt you're using that specifically affects test rendering in documentation.

2. (Multiple Select)

A) Examine the source data for the model to verify the presence of expected values.
B) Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
C) Implement a custom data test to specifically check the column for null values.
D) Switch the model materialization from 'view' to 'table'.

3. Your team decides to use separate schemas for development and production within the same data warehouse. Which of the following dbt features are essential for managing this workflow effectively?

A) Macros and Jinja templating to dynamically switch target schemas.
B) Environment variables to store environment-specific configurations.
C) dbt seeds to populate test data in your development schema.
D) dbt snapshots to compare data state between environments-

4. Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?

A) Unnecessary computations, as monthly models would rebuild daily data redundantly.
B) The DAG visualization would be too complex to understand.
C) Conflicts might arise due to models overwriting each other's data.
D) Testing would become impractical due to increased runtimes.

5. You're reviewing a pull request and notice a subtle logic error that wasn't caught in automated tests. How do you best approach this within the code review?

A) Simply fix the error directly in the pull request without leaving a comment.
B) Reject the pull request outright and ask the author to fix the error and resubmit.
C) Create a separate issue in the issue tracker, detail the bug, and reference it in the pull request.
D) Leave a comment explaining the error and provide a suggestion for how to fix it.

Solutions: