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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 Price: | $100 USD (varies by region) |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple choice, Multiple select |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | Not publicly disclosed (commonly reported ~60–70 questions) |
| Recommended Training: | dbt Learn dbt Documentation |
| Exam Registration: | dbt Labs official site dbt Certification Portal |
| 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 |
|---|---|
| Analytics Engineering Foundations | - SQL proficiency for analytics
|
| Deployment and Orchestration | - Running dbt in production
|
| Documentation and Lineage | - dbt documentation system
|
| dbt Core Concepts | - Project structure and configuration
|
| Testing and Data Quality | - Built-in and custom tests
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. After running dbt docs generate, you notice that your project documentation looks incomplete, and certain details seem to be missing. Which of the following actions could help you troubleshoot the issue?
A) Run the dbt clean command to clear out any outdated artifacts.
B) Ensure all relevant model directories are present under the top-level 'models' directory of your dbt project
C) Check if you've unintentionally used the -select flag to limit model inclusion during a previous dbt run.
D) Verify that all your models, sources, seeds, and tests have descriptions where appropriate.
2. You assume two columns of type 'numeric' will always align in terms of precision and scale (number of decimal places). What's a key way to design your models and tests to be resilient even if this assumption changes?
A) Write tests to compare the column metadata rather than their contents.
B) Always store all numeric values as strings to prevent these issues.
C) Use explicit casting functions to enforce matching precision during calculations.
D) Avoid relying on exact equality when comparing values from these columns.
3. You've meticulously added descriptions and tests to your dbt models. However, after running dbt docs generate, the tests don't appear in the generated documentation. Which of the following reasons are most likely?
A) There is a bug in the version of dbt you're using that specifically affects test rendering in documentation.
B) Your tests have incorrect syntax, and dbt is silently skipping them during documentation generation.
C) The dbt_project.yml file may not have the appropriate settings for including tests in the documentation.
D) You are using a custom schema test macro that isnt configured to be included in the documentation.
4. A sudden data quality issue occurs in production. You need to quickly reproduce the problem in a lower environment but retain a point-in-time snapshot of production dat a. Which strategies might you employ? Create a backup of the production database and restore it to a separate development or testing database.
A) staging area.
B) Use dbt snapshots to capture critical production tables during a defined timeframe for later use in troubleshooting.
C) A combination of the above, depending on time constraints and available resources.
D) Utilize data replication features (if supported by your data warehouse) to create a near real-time mirror of production data in a
5. Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?
A) All of the above.
B) Source freshness checks are configured incorrectly, allowing older data to be processed
C) There are dependency issues in your DAG affecting execution order.
D) Some models have incorrect materializations, causing them to not update when expected
Solutions:
| Question # 1 Answer: B,C,D | Question # 2 Answer: C,D | Question # 3 Answer: C,D | Question # 4 Answer: C | Question # 5 Answer: A |
By Merry
