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

dbt-Analytics-Engineering real exams

Exam Code: dbt-Analytics-Engineering

Exam Name: dbt Analytics Engineering Certification Exam

Updated: Aug 10, 2026

Q & A: 359 Questions and Answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Creating and Maintaining Documentation10%- Documentation standards
- Descriptions and metadata
- Generating documentation
Topic 2: Managing Data Pipelines15%- Pipeline orchestration
- Deployment strategies
- CI/CD integration
Topic 3: Implementing dbt Tests10%- Built-in tests
- Test configuration and execution
- Custom tests
Topic 4: Leveraging dbt State5%- State management
- State-aware operations
Topic 5: Developing dbt Models20%- Model design and structure
  • 1. Ref and source functions
  • 2. Materialization types
  • 3. Incremental models
- Model optimization
  • 1. Model contracts
  • 2. Performance tuning
Topic 6: dbt Models Governance15%- Version control integration
- Project organization and structure
- Naming conventions and standards
Topic 7: Debugging and Error Resolution15%- Resolving data quality issues
- Identifying modeling errors
- Debugging techniques
Topic 8: External Dependencies10%- Using packages
- Managing snapshots
- External sources integration

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?

A) Manually validate the dbt_project.yml against a fixed template file after each edit
B) Install a YAML linter within their code editor to catch formatting and basic schema errors.
C) Have them write dbt schema tests for their configuration files.
D) Thoroughly review dbts documentation for a complete list of valid properties to memorize.


2. (Multiple Select)

A) You need to frequently load incremental data using dbt's snapshot feature.
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) Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-


3. (Multiple Select)

A) The nested CASE statement with repeated conditional checks is causing redundant calculations.
B) The Jinja templating itself introduces a significant processing overhead.
C) Dynamically generating column names in a loop is inefficient for most databases.
D) The fct_orders table might lack an appropriate index for the warehouse_code column.


4. You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A) An incorrect materialization setting on the model.
B) There may be a LIMIT statement leftover from development.
C) Upstream data has quality issues with null values or unexpected joins.
D) Your model has accidental WHERE conditions that are too restrictive.


5. You want to use a YAML anchor to reuse a standard block of descriptive text within multiple models. How would you implement this?

A) Create a YAML anchor within the relevant model file where the text should be reused.
B) Define a Jinja macro for the text block, and then call the macro within your descriptions.
C) Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.
D) YAML anchors are not directly supported in the context of dbt model and source descriptions-


Solutions:

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

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