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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A) Define custom macros in the project file to encapsulate commonly used SQL patterns.
B) Include a docs: section with detailed descriptions of core models and transformations.
C) Set up pre-commit hooks within the project file to enforce linting and formatting rules.
D) Configure project-level tests to ensure adherence to data quality standards.

2. (Multiple Select)

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

3. You've configured your project to include both schema and data tests in the generated documentation. A user reports the tests are visible but don't show any execution results (pass/fail status). Why might this be happening?

A) The type of test you're using is not supported for displaying results in the documentation.
B) Your data pipeline hasn't been executed recently, so there are no recent test results.
C) The project settings are explicitly configured to hide test results in the documentation.
D) The documentation generator has a known limitation on how test results are displayed.

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

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

5. You're collaborating with a third-party analytics vendor who requires access to a subset of processed data within your dbt project for integration into their platform. How might you approach this securely?

A) Set up a data sharing mechanism through your data warehouse, if it supports this functionality, enabling controlled access.
B) Generate a dedicated readout schema in your data warehouse with appropriate grants for the vendor's user
C) Utilize a dbt package to export specific models to CSV or JSON format and provide those files to the vendor
D) A combination of the above might be needed, depending on the vendor's integration capabilities-

Solutions: