dbt Labs dbt-Analytics-Engineering - 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. A new stakeholder needs to receive an email report generated from the results of a specific dbt model. Unfortunately, your Bl tool doesn't directly integrate with dbt. How could you address this?

A) Write a post-hook that queries the model, formats the results, and sends the email.
B) Modify the model to output a CSV file and schedule a task to email the file as an attachment
C) Within the model's SQLi write logic to insert the output data into a dedicated reporting table-
D) Develop a separate script, triggered after the dbt job, to handle report generation and emailing-

2. You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?

A) Version control system metadata (e.g., Git history)
B) dbt Cloud's built-in job monitoring features, if applicable
C) Custom logging built into your models
D) Specialized data observability platforms

3. You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?

A) Different regions may use varying currency exchange rates.
B) Time zones and daylight savings rules for timestamps could differ between data sources.
C) Encoding formats (UTF-8, etc.) may be inconsistent across source systems.
D) The presence of unexpected HTML tags within textual data fields.

4. You're setting up a CI/CD pipeline for dbt deployments. Which actions can help maintain data integrity and consistency between your development, staging, and production environments?

A) All of the above.
B) Enforce a branching strategy that mirrors environment progression (e.g., dev branch deploys to dev, etc. )
C) Utilize dbt logs and artifacts to track any changes made outside the standard deployment pipeline in each environment.
D) Implement automated tests that validate data quality and model assumptions across all environments.

5. You're planning your production dbt deployment strategy. When deciding between completely separate development and deployment environments vs. using different schemas within a single data warehouse, what are I key factors to weigh?

A) The desired level of isolation and access control granularity.
B) The need to collaborate between developers with varying skill levels.
C) Your data warehouse's pricing model and how it charges for resource usage.
D) The complexity of your dbt project and the number of models.

Solutions: