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

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1
Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A. Data warehouses might not be optimized for period-over-period comparisons.
B. Views cannot perform complex date manipulation required for YoY calculations.
C. Incremental materializations aren't suitable for trend analysis.
D. ref() functions can hinder performance when working with historical data.

Question #2
(Multiple Select)

A. Introduce CTEs within models to create logical groupings of transformations.
B. Materialize all models as 'views' for maximum flexibility and to avoid potential storage bottlenecks.
C. Consider common reporting needs when designing downstream models.
D. Minimize the number of intermediate models, even if it means complex SQL within a few large models.

Question #3
You're getting inconsistent errors when adding new models in your dbt_project.yml. You suspect typos or indentation issues. How can you proactively improve this situation?

A. Write tests covering various aspects of dbt_project.yml to proactively catch errors.
B. Use a code editor or IDE with YAML-specific linting and auto-formatting support.
C. All of the above.
D. Set up a pre-commit hook that runs a YAML validation script before committing changes.

Question #4
Your company wants to standardize pull request templates. Which information would be beneficial to include in such a template?

A. A section to link to relevant design documents or specifications.
B. Specific instructions for the reviewer, if certain aspects need special attention.
C. Checkboxes for confirming that tests have been run and documentation has been updated if necessary.
D. All of the above.

Question #5
The dataset you work with contains personally identifiable information (PII). Which types of additional tooling might be necessary for your project beyond a core dbt implementation?

A. Advanced statistical modeling tools.
B. Data masking or tokenization solutions.
C. Enhanced data access governance and auditing tools.
D. Automated ETL pipelines

Solutions: