dbt Labs dbt-Analytics-Engineering Valid Test : dbt Analytics Engineering Certification Exam

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. 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) Add a -select flag when using dbt run to execute only the modified model.
C) Modify the model SQL using the config.get() macro to dynamically control the column's behavior
D) Define separate configurations for the column under the model and use the enabled property.

2. You've updated a model's description in a YAML file to reflect a new business requirement. You've also added additional context to the column descriptions. After regenerating the documentation, you notice the model description has updated, but the column descriptions have not. What could be the most likely reasons?

A) You are viewing an older cached version of the documentatiom
B) The model's YAML file has incorrect syntax preventing column descriptions from rendering.
C) The documentation was not configured correctly to display column descriptions.
D) You forgot to run dbt docs generate after modifying the YAML file.

3. You're refactoring a dbt project with circular dependencies (e.g., Model A references Model B, which references Model A).
Select the most appropriate strategy:

A) Rewrite SQL logic to remove the circular references.
B) Create snapshots to break the dependency chain.
C) Add the -full-refresh flag to dbt run to force execution.
D) Introduce a redundant intermediate model to resolve the circularity.

4. You notice that your dbt jobs take longer than expected. You suspect potential issues with models referencing large raw tables unnecessarily. Which of the following strategies might help optimize performance? (Choose two)

A) Add more indexes to all the raw tables in your warehouse.
B) Introduce CTEs within your models to pre-filter raw data where possible.
C) Move the raw table into a separate staging schema and grant access only to necessary models.
D) Convert your most-used models to incremental materializations.

5. You are using seeds in your dbt project for static data lookups. During a dbt seed run, you encounter an error indicating a duplicate primary key value. What are the likely consequences?

A) Models directly referencing the problematic seed will fail during the next dbt run.
B) Seed data is considered non-essential, and the dbt seed command will complete with a warning.
C) Existing seed data will remain, but records with the conflict will not be inserted.
D) The entire dbt seedoperation will roll back, leaving no seed data updated.

Solutions: