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

  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Updated: Aug 12, 2026     Q & A: 359 Questions and Answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Creating and Maintaining Documentation10%- Descriptions and metadata
- Documentation standards
- Generating documentation
dbt Models Governance15%- Version control integration
- Project organization and structure
- Naming conventions and standards
Leveraging dbt State5%- State management
- State-aware operations
Implementing dbt Tests10%- Custom tests
- Test configuration and execution
- Built-in tests
Developing dbt Models20%- Model design and structure
  • 1. Incremental models
  • 2. Ref and source functions
  • 3. Materialization types
- Model optimization
  • 1. Model contracts
  • 2. Performance tuning
Managing Data Pipelines15%- Deployment strategies
- CI/CD integration
- Pipeline orchestration
Debugging and Error Resolution15%- Identifying modeling errors
- Debugging techniques
- Resolving data quality issues
External Dependencies10%- External sources integration
- Using packages
- Managing snapshots

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need to grant readonly access to the results of your dbt project to stakeholders outside your team. Considering security, which approach would be the MOST suitable?

A) Regularly run dbt seed to export CSV files, which are then shared with stakeholders.
B) Have stakeholders directly query the dbt models, providing access to the production data warehouse.
C) Embed dbt model results within a Bl dashboard and manage access through the Bl tool.
D) Create a designated reporting schema and set up views that map to the dbt model outputs


2. (Multiple Select)

A) Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-
B) Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.
C) Upstream data sources update on a predictable cadence (e.g., daily extracts).
D) You need to frequently load incremental data using dbt's snapshot feature.


3. While refactoring models, you accidentally drop a table in your development environment that mirrors production. Which inherent characteristic of development environments likely contributed to this issue?

A) Development environments often have less stringent permissions to facilitate experimentation.
B) Development datasets might be incomplete or outdated compared to productiom
C) Both A and C-
D) Developers working in development environments might expect the ability to make and revert changes with fewer consequences.


4. You're refactoring a project with long model files containing intertwined logic. Which of the following is NOT a typical sign of a good candidate for applying modularity principles?

A) The same SQL patterns are repeated with minor variations.
B) The models are relatively small and perform simple transformations.
C) Renaming code blocks would improve readability.
D) Portions of the logic form distinct conceptual steps in the transformation process.


5. You need a way to document relationships between raw source tables, dbt models, and downstream Bl dashboards. What type of tool would be best suited for this task?

A) A data profiling tool
B) A version control system
C) A data catalog
D) dbt's built-in documentation (dbt docs)


Solutions:

Question # 1
Answer: C,D
Question # 2
Answer: A,C
Question # 3
Answer: C
Question # 4
Answer: B
Question # 5
Answer: C

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