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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Defining source freshness thresholds to catch issues with missing tables or views-
B) Implementing a robust suite of schema tests to validate object references-
C) Setting up a linter for your SQL files, either as part of your IDE or code editor
D) Using the 'defer' option to partially run a DAG and expose errors early

2. You've included some custom visualizations within your dbt project documentation (e.g., network graphs) but notice they don't render correctly when the site is generated. What might be the cause?

A) Custom visualizations are not directly supported by dbt's built-in documentation.
B) The dbt docs generate command has a strict limit on the file size of visualizations it can process-
C) Custom visualizations usually require additional JavaScript libraries to be included.
D) Your visualization definitions haven't been configured correctly in the project YAML files

3. You want to experiment with a new column in an existing model without risking production changes. Which configuration strategy in your dbt_project.yml would support this safely?

A) Utilize git branches and have different dbt_project.yml files for your production and experimental branches.
B) Define separate configurations for the column under the model and use the enabled property.
C) Modify the model SQL using the config.get() macro to dynamically control the column's behavior
D) Add a -select flag when using dbt run to execute only the modified model.

4. You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?

A) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
B) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
C) Add a depends_on relationship pointing to the potentially late table within your model.
D) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.

5. You discover a flaw in a data cleaning process applied early in your raw data transformation. What implications does this have for the rest of your dbt pipeline and the distinction between production vs. development?

A) All of the above.
B) There's a risk that incorrect data exists in both development and production, leading to unreliable modeling.
C) It highlights the importance of testing and validation at each transformation stage, not just in the final reporting layer.
D) You might need to re-run models downstream of the fix across both development and production to ensure data integrity.

Solutions: