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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

A downstream process relies on a critical model to be fully refreshed daily. How can you enforce this behavior using dbt_project.yml configuration?

A. Apply a post-hook that triggers a full refresh of the model.
B. Configure a schedule property within the models: section.
C. Set the model's materialized property to incremental _ refresh-
D. Update the model configuration to materialized: table and add a persist_docs: true property.

Question 2

(Multiple Select)

A. Increase the query_comment timeout setting in your dbt_project. yml
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. Apply an incremental materialization strategy to subsequent models if appropriate.

Question 3

You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

A. Create a source with a loaded_at_field and write custom logic to snapshot API data.
B. Write a dbt macro to encapsulate the API interaction logic.
C. Utilize a dbt package tailored for the specific API, if available.
D. Create a seed file to load the data directly into a table.

Question 4

You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?

A. The documentation generator is caching the old version of the macro.
B. Your macro implementation has a subtle logic error.
C. Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.
D. The macro might only be applied to specific model types.

Question 5

A model depends heavily on geospatial calculations, which are computationally expensive. What optimization strategy might be worth exploring?

A. Convert the model to a snapshot to leverage historical data more effectively.
B. Investigate whether the database offers specialized geospatial indexes or functions.
C. Pre-calculate common geospatial results as an incremental model.
D. Ensure the underlying data adheres to DRY principles.

Solutions: