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dbt Labs dbt-Analytics-Engineering Q&A - in .pdf
- Exam Code: dbt-Analytics-Engineering
- Exam Name: dbt Analytics Engineering Certification Exam
- Updated: Sep 20, 2026
- Q & A: 359 Questions and Answers
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: dbt Fundamentals | 15% | - dbt Core vs dbt Cloud - dbt project structure - dbt workflow and best practices |
| Topic 2: Models | 25% | - Sources and references - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Snapshots - Seeds |
| Topic 3: Data Transformation Techniques | 25% | - Common table expressions and subqueries - Refactoring and incremental models - Jinja templating - Macros and packages |
| Topic 4: Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - dbt docs and DAG visualization - Documentation generation |
| Topic 5: Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Git version control integration |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question #1
You're debugging a runtime error. Analyzing the compiled SQL alone doesn't provide enough clues to find the root cause. What's your next debugging step?
- A. Examine the dbt logs for more context about the model's execution and dependencies.
- B. Compare compiled SQL from a previous successful run to the current failing version-
- C. Disable custom macros to see if one of them is interfering-
- D. Modify the compiled SQL directly and re-run the query against your warehouse.
Correct Answer: A 🗳️
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Question #2
The dataset raw user activity is critical to several models. You've been informed that a major schema change is scheduled for this source. Select the best way to proactively mitigate the potential impact of this change on your dbt project.
- A. Create custom data tests that validate the critical columns in raw_user_activity.
- B. Pause all dbt jobs until the schema change risk is fully assessed.
- C. Run dbt test after the schema changes and address any failures.
- D. Communicate with the relevant data team to create a temporary staging table mirroring the old schema.
Correct Answer: A 🗳️
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Question #3
Performance Bottleneck
- A. Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
- B. Implement database-level indexes on columns involved in the joins, if appropriate.
- C. Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
- D. Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
Correct Answer: A,D 🗳️
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Question #4
(Multiple Select)
- A. Tests must be explicitly included in a model's test property for them to block model creation.
- B. Your dbt run command includes flags that override failing tests.
- C. Your tests are likely configured to raise warnings rather than cause true errors.
- D. A 'post-hook' in your project configuration automatically fixes the issues flagged by tests.
Correct Answer: B,C 🗳️
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Question #5
You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?
- A. Downstream models will inherit the performance issues.
- B. The models are at risk of failing if the source data grows substantially.
- C. This behavior automatically triggers an optimization feature within dbt.
- D. These models likely have issues with joins or CTE structure impacting performance.
Correct Answer: B,D 🗳️
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