Passing exam with dbt-Analytics-Engineering latest training materials
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Updated: Sep 07, 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 |
| Passing Score: | 65% |
| Exam Duration: | 120 minutes |
| Exam Price: | 200 USD |
| Real Exam Qty: | 65 |
| Exam Format: | Multiple choice, Online proctored |
| Available Languages: | English |
| Related Certifications: | dbt Architect Certification |
| 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 |
|---|---|---|
| Developing dbt Models | 20% | - Model design and structure
|
| Leveraging dbt State | 5% | - State-aware operations - State management |
| dbt Models Governance | 15% | - Version control integration - Project organization and structure - Naming conventions and standards |
| External Dependencies | 10% | - External sources integration - Managing snapshots - Using packages |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Identifying modeling errors - Debugging techniques |
| Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
| Managing Data Pipelines | 15% | - Pipeline orchestration - CI/CD integration - Deployment strategies |
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Generating documentation - Documentation standards |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question 1
You introduce a new test, which unexpectedly leads to many models previously deemed 'successful' now failing.
What should your initial troubleshooting steps include?
A. Verify the test logic doesn't contain errors.
B. Temporarily raise the test's failure threshold to give you time to investigate.
C. Review the test's severity configuration (warning vs_ error).
D. Check if the test is revealing previously overlooked data quality problems.
Question 2
You're integrating a new dbt package and find its models conflict with naming conventions in your existing project. Which approach generally provides the most flexible solution?
A. Utilize the dbt_project.yml file to re-alias the models provided by the package.
B. Contact the package maintainers to request changes aligning with your conventions.
C. Manually edit the SQL within the conflicting package models.
D. Override the package's macros and redefine them within your project.
Question 3
Collaborative Fix
A. All of the above.
B. Open a pull request with detailed comments explaining the rationale for the changes and any potential side-effects.
C. Add clear commit messages that concisely summarize the purpose of the fix.
D. Include updated model documentation (dbt docs) outlining the logic and reasons for adjustment.
Question 4
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?
A. Too many tests are being defined on individual models.
B. The macro is accidentally triggering downstream model rebuilds.
C. This is expected behavior, and custom tests always incur a performance penalty.
D. The macro's SQL logic is inefficiently querying the model or other data.
Question 5
(Multiple Select)
A. Ensuring column ordering matches an external specification.
B. Enforcing specific ratios or relationships between aggregated values.
C. Validating that the row count of the staging model falls within an expected range.
D. Checking for referential integrity with a separate lookup table.
Solutions:
| Question 1 Answer: A,C,D | Question 2 Answer: A | Question 3 Answer: A | Question 4 Answer: A,D | Question 5 Answer: B,C,D |
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