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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You've defined a complex custom singular test. While testing it initially on a development dataset, it times out. Which optimization techniques might be worth trying?

A) Ensure the test SQL uses appropriate indexes in the underlying database.
B) Rewrite the test using a dbt macro for better maintainability.
C) Disable the test in production environments and run it only in development.
D) Remove any redundant joins or subqueries from the test logic.

2. Which of the following scenarios would likely require you to use the -no-compile flag with the dbt docs generate command?

A) You've made syntax errors in your model definitions and want to prevent documentation errors due to compilation failures.
B) Running the documentation generation process in a production setting where compilation could introduce risks.
C) Adding a new model and its corresponding description in your project.
D) Updating existing column descriptions to reflect recent upstream changes.

3. You must exclude a specific model from being included in the generated documentation entirely. How would you achieve this?

A) Add a dæs: {enabled: false} entry to the model's YAML configuration.
B) Use the enabled flag in your dbt_projectyml file to control model inclusion.
C) Remove all descriptions and tags associated with the model.
D) Its not possible to exclude individual models; you can only exclude entire folders.

4. (Multiple Select)

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

5. (Multiple Select)

A) Schema tests to confirm the existence of tables and columns.
B) not_null and unique tests on primary key columns.
C) Tests comparing the latest data against historical baselines.
D) Tests checking for row count discrepancies between source systems and target tables.

Solutions: