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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You try updating a column description using its corresponding YAML file. After regenerating the docs, the update doesn't appear. Which of the following might be the cause?

A) Your browser is aggressively caching the documentation site.
B) There could be a syntax error within the updated description that dbt is silently ignoring.
C) Changes in column descriptions only update the database schema, not the project documentation.
D) The column description might need additional configuration with a test for it to render in the docs.

2. (Multiple Select)

A) You want to test different loading strategies for a large source table.
B) You need to apply custom column-level transformations to a raw source.
C) The source has multiple tables with related schemas requiring standardization.
D) The source data is sensitive and requires role-based access control.

3. You've defined a unique test on a key column within a model. When running dbt test, you encounter a surprising failure. Which of the following might be the cause?

A) An incorrect join in your model is inadvertently creating duplicate rows.
B) Upstream data was updated unexpectedly, leading to previously non-existent duplicates.
C) The unique test is too strict and doesn't align with the business definition of uniqueness.
D) There are truly duplicate values in the column.

4. You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A) Whether the tool can handle the full complexity of your data transformations.
B) The ease of migrating existing dbt projects into the visual tool.
C) If the visual tool has advanced machine learning capabilities for data cleaning.
D) The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.

5. A model intermittently fails. Sometimes it runs without errors. Which category of issue should you suspect FIRST?

A) Race conditions due to models in your DAG running concurrently without proper synchronization.
B) Data quality issues in the source that are not consistently present.
C) Errors within complex macros used by the model, triggered only under specific data conditions.
D) dbt-specific configuration errors causing unpredictable behavior at the engine level.

Solutions: