dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question 1

You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?

A. Different database configurations or resource allocations (memory, CPU, etc.) between environments-
B. Production data volume being significantly larger than development data.
C. Inefficient use of snapshots or incremental models in your production dbt project_
D. Network latency impactingressing access to remote data sources.

Question 2

(Multiple Select)

A. Enforcing specific ratios or relationships between aggregated values.
B. Ensuring column ordering matches an external specification.
C. Checking for referential integrity with a separate lookup table.
D. Validating that the row count of the staging model falls within an expected range.

Question 3

(Multiple Select)

A. Configure separate dbt jobs with different schedules tailored to the model groups.
B. Use dbt tags to group models based on update frequency
C. Employ dbt snapshots to selectively run only models that depend on changed sources.
D. Have all models run nightly but use where conditions within models to limit work when data is unchanged.

Question 4

You identify the need to refactor several models with similar transformations. Which of the following is the first step to increase modularity and promote the DRY philosophy?

A. Use a text editor's "find and replace" function to update repetitive code blocks.
B. Test individual models thoroughly to establish a baseline before making changes.
C. Create CTEs to segment logical steps within the existing model SQL.
D. Analyze commonalities and differences in the transformations to determine refactoring potential.

Question 5

You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?

A. Customize the schema property in your dbt_project.yml to namespace dbt-generated objects.
B. Manually rename the conflicting tables in the database prior to running any dbt jobs.
C. Add configuration settings to your database connection profile instructing dbt to use a specific schema prefix.
D. Update your dbt models to use aliases that avoid the name collisions.

Solutions: