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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Integrating dbt's logging with your existing monitoring and alerting platforms.
B) Require thorough code reviews with a focus on testability before deploying model changes to production.
C) Implement custom metrics tracking the duration and resource usage of individual dbt models.
D) Using dbt Cloud's built-in job monitoring and notification features, if applicable.

2. You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?

A) Your tests have incorrect severity thresholds.
B) The tests are defined in seeds.csv files.
C) Your dbt project has multiple targets configured.
D) You haven't compiled your project since writing the new tests.

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

4. You need to give read-only access to a specific dataset in your production data warehouse to a data analyst on your team. How might you approach this using dbt environments?

A) Modify the data warehouse permissions directly to grant access to the required dataset.
B) Use row-level security features in your data warehouse and dynamically filter data in models using environment variables.
C) A combination of the above, depending on the capabilities of your data warehouse.
D) Create a dedicated dbt target for the analyst and utilize grants within your models to control access.

5. You're setting up a dbt project with a model that generates a large intermediate result. You're concerned about performance. Which approaches could be helpful?

A) Deploy the dbt project to a smaller development environment to reduce processing overhead
B) Investigate database-specific features (e.g., temporary tables ) and refactor dbt logic to leverage them.
C) Schedule the model to run only during off-peak hours.
D) Use a staging schema for the intermediate model and adjust the job's materialization settings.

Solutions: