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Updated: Aug 31, 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 |
| Related Certifications: | dbt Analytics Engineering Certification |
| Available Languages: | English |
| Passing Score: | 80% |
| Exam Format: | Multiple choice, Multiple select |
| Exam Price: | USD 200 |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 65 |
| Sample Questions: | dbt Labs dbt-Analytics-Engineering Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No formal prerequisites, but practical experience with dbt and SQL is recommended |
| Official Syllabus URL: | https://www.getdbt.com/certification |
dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
| Data Transformation Techniques | 25% | - Common table expressions and subqueries - Jinja templating - Refactoring and incremental models - Macros and packages |
| Models | 25% | - Seeds - Sources and references - Writing and managing SQL models - Snapshots - Materializations (table, view, ephemeral, incremental) |
| Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Git version control integration - Jobs and scheduling in dbt Cloud |
| Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question 1
(Multiple Select)
A. The technology used for your production data warehouse.
B. The overall size and complexity of your dbt project
C. The need for feature parity (functions, syntax) with the production warehouse.
D. The cost-efficiency of the development environment solution.
Question 2
"Isn't debugging harder with many smaller models in a DAG?" How would you address this concern expressed by a colleague?
A. "We'll rely extensively on snapshots to trace errors back to their origin."
B. "Smaller models are easier to test individually, leading to faster issue identification."
C. "Debugging is slightly harder, but the benefits outweigh the added effort."
D. "Our DAG will be simple, so debugging model dependencies won't be a concern."
Question 3
You're working with a dataset where a column is expected to be updated frequently with newly arriving events. Which type of test would be MOST suitable to check that this process is working as intended?
A. A generic 'not_null' test on the column.
B. A custom test comparing timestamps on the column in your dbt model to the current time.
C. A test comparing the row count of the source table between different dbt runs.
D. A schema test on the column in the source table.
Question 4
A colleague asks, "Why do we need dbt docs when we already have good database documentation?" Provide a compelling response.
A. dbt docs generates dynamic documentation that directly reflects your project's dependencies and current state.
B. dbt docs is mandatory for version control with dbt projects.
C. dbt docs primarily serves as a replacement for dbt seed.
D. dbt docs automatically tracks model testing results.
Question 5
While explaining dbt concepts to a colleague, they ask, "Don't macros just add an extra layer of complexity?" How would you address their concern?
A. "Macros are primarily needed for legacy projects; new projects should avoid them."
B. "Yes, they do, but their maintainability benefits outweigh the initial complexity."
C. "Macros don't introduce additional complexity but are mainly used for performance tuning."
D. "Macros simplify debugging because they break down your dbt model logic."
Solutions:
| Question 1 Answer: A,C,D | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: B |
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