dbt Labs dbt-Analytics-Engineering Questions & Answers - in .pdf
- Total Q&A: 359
- Update: Aug 02, 2026
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- Vendor: dbt Labs
- Exam Code: dbt-Analytics-Engineering
- Exam Name: dbt Analytics Engineering Certification Exam
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dbt Labs dbt-Analytics-Engineering Q&A - Testing Engine
- Total Q&A: 359
- Update: Aug 02, 2026
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- Exam Code: dbt-Analytics-Engineering
- Exam Name: dbt Analytics Engineering Certification Exam
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Developing dbt Models | 20% | - Model optimization
- 1. Model contracts
- 2. Performance tuning
- Model design and structure
- 1. Incremental models
- 2. Materialization types
- 3. Ref and source functions
|
| Topic 2: Creating and Maintaining Documentation | 10% | - Documentation standards
- Generating documentation
- Descriptions and metadata
|
| Topic 3: Managing Data Pipelines | 15% | - Pipeline orchestration
- Deployment strategies
- CI/CD integration
|
| Topic 4: Implementing dbt Tests | 10% | - Custom tests
- Built-in tests
- Test configuration and execution
|
| Topic 5: dbt Models Governance | 15% | - Version control integration
- Project organization and structure
- Naming conventions and standards
|
| Topic 6: External Dependencies | 10% | - Managing snapshots
- External sources integration
- Using packages
|
| Topic 7: Debugging and Error Resolution | 15% | - Resolving data quality issues
- Debugging techniques
- Identifying modeling errors
|
| Topic 8: Leveraging dbt State | 5% | - State management
- State-aware operations
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. (Multiple Select)
A) Utilize a YAML validator tool independently of dbt to check for general formatting errors.
B) If available, compare your schema.yml to a previous working version to spot problematic changes.
C) Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
D) Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.
2. After merging a large pull request, you notice a critical issue introduced in one of the commits. What actions might be necessary?
A) Use git cherry-pick to apply specific commits from the reverted change to another branch, effectively restoring the necessary parts.
B) Use git revert to create a new commit that specifically undoes the problematic changes.
C) All of the above could be needed as part of the fix.
D) Identify the problematic commit using git log or git blame.
3. You encounter an unexpected dbt run error. Rather than re-running all models, you want to resume from the point of failure. Which of the following, if any, might allow for this?
A) dbt Cloud's job retry features for automatically resuming failed job executions.
B) Using dbt snapshots to track model states, allowing you to rollback changes as needed
C) The --fail-fast flag instructs dbt to only execute successful models, making restarts unnecessary
D) dbt unfortunately lacks native support for resumable jobs.
4. You have a large dataset that undergoes frequent, small updates. You need a model to quickly reflect these changes while minimizing resource usage. Which standard dbt materialization would be the BEST fit?
A) Table
B) Ephemeral
C) Incremental
D) View
5. You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?
A) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
B) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
C) Add a depends_on relationship pointing to the potentially late table within your model.
D) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
Solutions:
Question # 1 Answer: A,C | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: A,B |
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