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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

A) Reference an external Markdown file within the source description.
B) Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
C) Write a longer description directly within the YAML file, breaking it across multiple lines.
D) dbt documentation does not support multi-paragraph descriptions for sources.

2. A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?

A) It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
B) dbt docs generate -select [model_name]
C) Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
D) dbt docs generate --models +[model_namel

3. You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?

A) Use views to create different projections of the core tables, exposing only the necessary columns.
B) Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.
C) Create incremental models that directly remove the sensitive columns before reporting models utilize them.
D) Apply row-level security within the data warehouse itself for fine-grained control.

4. You're setting up CI/CD for your dbt project to automate deployments. Which aspects of your testing strategy might need adaptation for a production-focused CI/CD workflow?

A) Emphasizing tests with longer run times for thorough checks before deployment.
B) Disabling all tests that rely on creating new tables, to avoid impacting the production database.
C) Prioritizing tests with strict thresholds to avoid false positives from minor data variations in production.
D) Including smoke tests that run in production to verify core functionality after a deployment.

5. (Multiple Select)

A) The model's data needs to be frequently joined with other datasets for analysis.
B) You need to grant access to a model's results to users with limited permissions.
C) Long-term storage of the model's output isn't a primary requirement.
D) The model involves complex transformations that are computationally expensive.

Solutions: