dbt Labs dbt-Analytics-Engineering Questions & Answers - in .pdf
- Total Q&A: 359
- Update: Jul 28, 2026
- Price: $59.99
- Vendor: dbt Labs
- Exam Code: dbt-Analytics-Engineering
- Exam Name: dbt Analytics Engineering Certification Exam
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- Total Q&A: 359
- Update: Jul 28, 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 Exam Overview:
| Certification Vendor: | dbt Labs |
| Exam Name: | dbt Analytics Engineering Certification Exam |
| Exam Number: | dbt-analytics-engineering-certification |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple choice, Multiple select |
| Real Exam Qty: | Not publicly disclosed (commonly reported ~60–70 questions) |
| Exam Price: | $100 USD (varies by region) |
| Certificate Validity Period: | 2 years |
| Recommended Training: | dbt Learn dbt Documentation |
| Exam Registration: | dbt Certification Portal dbt Labs official site |
| Sample Questions: | dbt Labs dbt-Analytics-Engineering Sample Questions |
| Exam Way: | Online proctored exam (via dbt Labs certification platform / authorized proctoring provider) |
| Pre Condition: | No formal prerequisites required; strong SQL and data modeling knowledge recommended |
| Official Syllabus URL: | https://www.getdbt.com/certification |
dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deployment and Orchestration | - Running dbt in production
|
| Topic 2: Analytics Engineering Foundations | - SQL proficiency for analytics
|
| Topic 3: Documentation and Lineage | - Data lineage understanding
|
| Topic 4: dbt Core Concepts | - Models and materializations
|
| Topic 5: Testing and Data Quality | - Data validation strategies
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?
A) In environments where strict data governance and transformation processes are in place.
B) Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
C) When performance is a top priority, and the overhead of additional data transformations is undesirable.
D) When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
2. You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?
A) The accepted_values test configuration has the wrong column specified.
B) Your test case is accidentally permitting null values, which bypass the check.
C) The model's data type isn't compatible with the accepted_values test.
D) A downstream process is overwriting the results after the model is built.
3. The results of a dbt model contain values outside the expected range. You suspect an issue with Jinja templating. Where might this issue originate?
A) In the model's SQL, where you're using Jinja for calculations or dynamic logic.
B) In a macro used by the model.
C) All of the above.
D) Within a dbt_projectyml model or source configuration.
4. Which of the following scenarios would typically warrant the use of production data in a development environment?
A) Development environments should generally avoid using real production data to minimize risks.
B) Training new members of your team on writing SQL and basic dbt models.
C) Testing changes to a critical business reporting model for accuracy before deployment.
D) Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
5. You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?
A) Introduce a new intermediate table to store the result of the date calculations.
B) Continue copying the date calculation logic into each relevant model.
C) Create a macro, encapsulating the date calculations, and reference it in the affected models.
D) Write a custom Python function and import it into your dbt project.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A,B,C | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: C |
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