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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Integrating dbt with your CI/CD tool and using job-specific triggers or filters.
B) Using separate dbt profiles for production and development environments
C) Scheduling less frequent jobs using dbt Cloud's built-in job scheduler
D) Implementing pre-hooks and post-hooks to perform tasks before and after dbt model execution.

2. dbt Model Snippet:SQL

A) The source() function referencing data from the 'analytics' schema_
B) The literal use of 'analytics' as the target_database.
C) The Jinja conditional logic used for incremental logic.
D) The nested subquery to calculate MAX(created_at).

3. You're refactoring a complex dbt model and realize breaking it down into smaller models would improve readability. However, you're concerned about performance overhead due to the additional materializations. How would you address this concern?

A) Switch to incremental materializations for all intermediate models.
B) Profile your models to pinpoint bottlenecks and optimize those specifically.
C) Utilize ephemeral materializations for intermediate steps, where appropriate_
D) Rely on source freshness checks to determine if intermediate models need to be rebuilt_

4. You need to regularly run performance tests to benchmark a time-sensitive incremental model. These tests involve more complex setup than standard dbt tests. How could you efficiently integrate them into your workflow?

A) Convert them into dbt seeds to easily execute them alongside model runs-
B) Define the performance tests as their own dbt project, triggered separately-
C) Write the tests as generic tests and use dbt's built-in warning thresholds.
D) Create a dbt operation (macro) to encapsulate the test logic and invoke it as needed.

5. In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A) Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
B) In environments where strict data governance and transformation processes are in place.
C) When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
D) When performance is a top priority, and the overhead of additional data transformations is undesirable.

Solutions: