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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're working with ephemeral models in your dbt project. Which scenarios could cause an ephemeral model to fail?

A) Data quality tests defined within the model are not met.
B) An upstream source table is temporarily inaccessible.
C) A syntax error in the model's SQL code.
D) A downstream dependency of the ephemeral model fails.

2. 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) A combination of the above, depending on the capabilities of your data warehouse.
C) Create a dedicated dbt target for the analyst and utilize grants within your models to control access.
D) Modify the data warehouse permissions directly to grant access to the required dataset.

3. You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

A) The baseline dataset itself has become corrupted
B) Your tests are too strict and need more generous tolerances.
C) Your upstream ETL process has a logic error, leading to incorrect data.
D) There are differences in data types or time zone handling between the model and baseline.

4. A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

A) Snapshots ensure that historical data won't be impacted unintentionally.
B) By using macros, modifications are limited to a single location.
C) Incremental materializations will help verify that the changes are successful.
D) With clear dependencies, they can pinpoint which upstream models to test.

5. Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?

A) There are dependency issues in your DAG affecting execution order.
B) Source freshness checks are configured incorrectly, allowing older data to be processed
C) Some models have incorrect materializations, causing them to not update when expected
D) All of the above.

Solutions: