- Exam Code: dbt-Analytics-Engineering
- Exam Name: dbt Analytics Engineering Certification Exam
- Updated: Sep 13, 2026
- Q & A: 359 Questions and Answers
dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering exam
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 2: Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Topic 3: External Dependencies | 10% | - Managing snapshots - External sources integration - Using packages |
| Topic 4: Developing dbt Models | 20% | - Model design and structure
|
| Topic 5: dbt Models Governance | 15% | - Naming conventions and standards - Project organization and structure - Version control integration |
| Topic 6: Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Topic 7: Implementing dbt Tests | 10% | - Built-in tests - Custom tests - Test configuration and execution |
| Topic 8: Creating and Maintaining Documentation | 10% | - Generating documentation - Documentation standards - Descriptions and metadata |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question #1
You've defined a complex custom singular test. While testing it initially on a development dataset, it times out. Which optimization techniques might be worth trying?
- A. Ensure the test SQL uses appropriate indexes in the underlying database.
- B. Remove any redundant joins or subqueries from the test logic.
- C. Rewrite the test using a dbt macro for better maintainability.
- D. Disable the test in production environments and run it only in development.
Correct Answer: A,B 🗳️
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Question #2
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?
- A. Temporarily introduce a conditional branch into the macro code that is environment-aware.
- B. Add verbose logging within the macro to track execution differences step by step.
- C. Set up a CIICD test that runs the macro in isolation using both database versions.
- D. Use database-specific functions to query the version information directly from within dbt models.
Correct Answer: B,C,D 🗳️
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Question #3
Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?
- A. Run dbt test multiple times with varying filters based on desired severity levels.
- B. Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
- C. Use dbt tags for classifying tests, then selectively execute them.
- D. Rely on your data observability platform to differentiate and alert based on dbt test results.
Correct Answer: B,C,D 🗳️
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Question #4
During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?
- A. Strategically employ ephemeral models in development to avoid materializing intermediate data repeatedly.
- B. Use development environment settings that incrementally build models only when code changes.
- C. Temporarily decrease the sample size of data during development, while reverting to full data processing in other environments.
- D. All of the above.
Correct Answer: D 🗳️
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Question #5
You discover a flaw in a data cleaning process applied early in your raw data transformation. What implications does this have for the rest of your dbt pipeline and the distinction between production vs. development?
- A. It highlights the importance of testing and validation at each transformation stage, not just in the final reporting layer.
- B. There's a risk that incorrect data exists in both development and production, leading to unreliable modeling.
- C. All of the above.
- D. You might need to re-run models downstream of the fix across both development and production to ensure data integrity.
Correct Answer: C 🗳️
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