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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. While onboarding a new team member, you need to ensure consistency between their local development setup and your existing production dbt deployment. Which of these are essential?

A) Setting up access and permissions in the data warehouse for the new user, aligning with your team's security practices.
B) Enforcing version control and utilizing a requirements txt file to pin dbt and adapter dependencies.
C) All of the above.
D) Providing clear documentation on configuration of profiles_yml to connect to the correct targets.

2. After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A) Your tests were correct, but a bug in dbt prevented them from running successfully.
B) The tests you wrote were too strict, causing false positives.
C) There are logic errors in your dbt models that the tests didn't catch.
D) Critical assumptions were made about the data in the Bl layer, independent of dbt models.

3. 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.

4. (Multiple Select)

A) Ensure relevant columns in underlying tables have appropriate indexes.
B) Introduce redundant columns in source tables to store pre-calculated percentage changes.
C) Utilize window functions to efficiently compute the difference without self-joins.
D) Pre-calculate frequently used periods (e.g., current month, previous month) and store them as intermediate CTEs.

5. Your baseline comparison tests start failing intermittently. Data distributions change slightly from day to day, sometimes triggering the thresholds. Which strategy could make your tests more robust?

A) Rewriting your SQL to compare averages or medians instead of exact value counts-
B) Introducing configurable tolerances into your test definitions.
C) Removing the baseline comparison tests entirely, as they provide unreliable signals.
D) Scheduling the tests to run less frequently (e.g., weekly rather than daily).

Solutions: