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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A) Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
B) Enforce a strict character limit on source descriptions-
C) Split your source description into multiple properties (e.g., description_short and description_long)
D) Utilize Markdown within your descriptions to improve readability with headings and line breaks-

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

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

3. (Multiple Select)

A) Apply an incremental materialization strategy to subsequent models if appropriate.
B) Introduce a source snapshot to create a point-in-time copy of the data.
C) Use CTEs or views in the source definition to pre-filter columns or apply initial transformations.
D) Increase the query_comment timeout setting in your dbt_project. yml

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

5. "The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

A) All of the above.
B) Examine the source tables for data quality issues like duplicates or missing values.
C) Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
D) Test the model in isolation using the dbt run -select flag to narrow down dependencies.

Solutions: