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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?

A) The transformations are project-specific but used across multiple models.
B) The transformations are complex and involve domain-specific logic that might be beneficial to others.
C) You need to limit access to these transformations to a specific team within your organization.
D) The transformations are relatively simple and could be written as macros.

2. A project is migrating from a staging environment to a production environment within the same data warehouse. However, the production data has more strict access controls. Which approaches could you take to manage this transition?

A) Store connection credentials as environment variables and reference them within the profiles.yml using Jinja templating.
B) Utilize a secrets management solution and integrate it into your dbt deployment process.
C) Use separate profiles.yml files for staging and production, each with their respective credentials.
D) All of the above.

3. You define a source and later try to reference it. Compilation fails with: "Model depends on a node named 'my source' which was not found". What are some likely causes?

A) The source definition is missing or incorrect in your dbt_project.yml or sources file.
B) Your dbt_project.yml configuration has the incorrect source-paths setting.
C) All of the above.
D) There's a typo in how you're referencing the source name using source().

4. You introduce performance optimizations to a subset of models. You need to benchmark both old and new versions in an isolated environment before deployment. How might you structure your dbt commands for this comparison?

A) Within a model, conditionally write to different schemas based on an environment variable, preserving both versions.
B) Use feature branches and switch between them with different dbt profiles for old and new code.
C) Utilize dbt seed to populate separate datasets representing the old and new model outputs for side-by-side analysis.
D) Add dbt hooks to temporarily archive the original output tables before overwriting them during benchmark runs.

5. Performance Bottleneck

A) Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
B) Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
C) Implement database-level indexes on columns involved in the joins, if appropriate.
D) Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.

Solutions: