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Updated: Sep 27, 2026
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dbt Labs dbt-Analytics-Engineering Exam Overview:
| Certification Vendor: | dbt Labs |
|---|---|
| Exam Name: | dbt Analytics Engineering Certification Exam |
| Exam Number: | dbt-Analytics-Engineering |
| Certificate Validity Period: | 2 years |
| Exam Price: | 200 USD |
| Passing Score: | 65% |
| Related Certifications: | dbt Architect Certification |
| Available Languages: | English |
| Real Exam Qty: | 65 |
| Exam Format: | Online proctored, Multiple choice |
| Exam Duration: | 120 minutes |
| Recommended Training: | dbt Official Documentation dbt Certification Study Guide |
| Exam Registration: | Official Registration Page |
| Sample Questions: | dbt Labs dbt-Analytics-Engineering Sample Questions |
| Exam Way: | Online, remotely proctored; delivered via Caveon testing platform |
| Pre Condition: | SQL proficiency recommended; 6+ months practical experience with dbt Core or dbt Cloud suggested; no mandatory prerequisites |
| Official Syllabus URL: | https://www.getdbt.com/certifications/analytics-engineer-certification-exam |
dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 2: dbt Models Governance | 15% | - Project organization and structure - Version control integration - Naming conventions and standards |
| Topic 3: Creating and Maintaining Documentation | 10% | - Documentation standards - Generating documentation - Descriptions and metadata |
| Topic 4: Debugging and Error Resolution | 15% | - Identifying modeling errors - Resolving data quality issues - Debugging techniques |
| Topic 5: Managing Data Pipelines | 15% | - Deployment strategies - CI/CD integration - Pipeline orchestration |
| Topic 6: Developing dbt Models | 20% | - Model optimization
|
| Topic 7: Implementing dbt Tests | 10% | - Custom tests - Test configuration and execution - Built-in tests |
| Topic 8: External Dependencies | 10% | - External sources integration - Managing snapshots - Using packages |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
You introduce a custom macro into your project. This is a snippet of your dbt_project.yml:YAML
- A. The macro_paths configuration in your dbt_project.yml might be incorrect or missing.
- B. You must place the macros directory within the root of your dbt project.
- C. The macro's name should only be 'trim', without the 'string_utils' prefix.
- D. The .sql extension on the file within the macros directory is incorrect.
Correct Answer: A 🗳️
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A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?
- A. Ephemeral
- B. Incremental
- C. Table
- D. View
Correct Answer: C 🗳️
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A model depends heavily on geospatial calculations, which are computationally expensive. What optimization strategy might be worth exploring?
- A. Ensure the underlying data adheres to DRY principles.
- B. Convert the model to a snapshot to leverage historical data more effectively.
- C. Pre-calculate common geospatial results as an incremental model.
- D. Investigate whether the database offers specialized geospatial indexes or functions.
Correct Answer: D 🗳️
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Your data warehouse environment undergoes a major version upgrade, introducing changes in SQL syntax and supported functions. How would you handle the migration of dbt projects to ensure compatibility?
- A. All of the above.
- B. Utilize custom macros to introduce conditional logic or abstract away syntax differences between versions, if feasible.
- C. Thoroughly test all dbt models across your staging and development environments to identify necessary updates.
- D. Create temporary branches in your dbt projects to isolate upgrade-related code changes for each environment
Correct Answer: A 🗳️
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Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?
- A. Employ a secrets management tool and integrate it with your dbt deployment.
- B. Utilize environment variables to store seed data values.
- C. Create separate seed files and add them to your .gitignore or equivalent.
- D. Write the seed data into log files, excluding them from version control.
Correct Answer: C 🗳️
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