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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need to give read-only access to a specific dataset in your production data warehouse to a data analyst on your team. How might you approach this using dbt environments?

A) Use row-level security features in your data warehouse and dynamically filter data in models using environment variables.
B) Create a dedicated dbt target for the analyst and utilize grants within your models to control access.
C) A combination of the above, depending on the capabilities of your data warehouse.
D) Modify the data warehouse permissions directly to grant access to the required dataset.

2. Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?

A) Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
B) Rely on your data observability platform to differentiate and alert based on dbt test results.
C) Run dbt test multiple times with varying filters based on desired severity levels.
D) Use dbt tags for classifying tests, then selectively execute them.

3. You encounter the following source configuration in a dbt_project.yml file. What kind of data source does this represent?

A) A CSV file loaded as a seed.
B) An output from another dbt model.
C) An external data source connected via a dbt adapter.
D) A raw table from your data warehouse.

4. You need to roll out a database migration mid-month. It requires changes to some of your dbt model schemas. How would you manage a smooth transition?

A) Deploy model changes alongside database changes, accepting potential errors for a period.
B) Pause all dbt jobs until after the database migration is fully completed.
C) Duplicate models, modifying them for the new schema, and switch over in your job configuration.
D) Use feature branches and environment-aware logic in dbt to allow pre- and post-migration versions to coexist temporarily.

5. You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?

A) Always store all numeric values as strings to prevent these issues.
B) Write tests to compare the column metadata rather than their contents.
C) Avoid relying on exact equality when comparing values from these columns.
D) Use explicit casting functions to enforce matching precision during calculations.

Solutions: