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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?

A. Dbt view materializations behave differently in production.
B. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
C. Your dbt job didn't run successfully, so the views were created empty.
D. Permissions granted in production don't allow reading data from the underlying tables.

Question 2

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

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

Question 3

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. With clear dependencies, they can pinpoint which upstream models to test.
C. By using macros, modifications are limited to a single location.
D. Incremental materializations will help verify that the changes are successful.

Question 4

You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?

A. rely on native version history features in your data warehouse (if available).
B. Write a dbt macro to create a temporary table with historical data to compare against.
C. Use a snapshot to preserve a point-in-time reference of the column.
D. Use a custom 'accepted_values' test together with a dbt seed.

Question 5

Regular test runs reveal inconsistent and intermittent test failures, despite no obvious changes to models or source dat a. What debugging strategies might you use?

A. Examine data warehouse performance metrics for resource constraints during test runs.
B. Use dbfs logging feature to add detailed output to the test execution.
C. Temporarily switch to a more consistent testing environment.
D. Consider if external dependencies used within dbt tests are themselves unstable.

Solutions: