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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Add {{ log(macros, info=true) statements within the macro to track how it executes-
B) Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
C) Check the generated column names in the compiled SQL to ensure they match existing columns in the data.
D) Implement unit tests specifically for the macro to validate its output under different inputs

2. You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?

A) Creating multiple deployment environments in dbt Cloud.
B) dbt profiles defined in your dbt_project.yml file.
C) Using dbt tags to selectively execute parts of your project.
D) dbt's -vars flag to override variable values.

3. You're considering adding workflow orchestration to manage dependencies between your dbt project, data loading tasks, and the triggering of downstream Bl updates. Which of the following types of tools would be most suitable for this?

A) Data quality monitoring tools
B) General-purpose workflow orchestration tools (e.g., Airflow, Prefect)
C) Data lineage tools
D) CI/CD platforms

4. While setting up profiles.yml, a colleague suggests hardcoding database passwords directly. You propose alternative approaches for the sake of security. Which of the following are valid arguments in favor of your approach?

A) Using environment variables or secrets managers improves maintainability and auditability of credentials.
B) All of the above.
C) Hardcoded credentials can be accidentally committed to version control, posing a security risk.
D) Credentials often need to be rotated, and hardcoding them would make updates cumbersome.

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

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

Solutions: