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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Documentation and Lineage | - dbt documentation system
|
| Topic 2: Testing and Data Quality | - Data validation strategies
|
| Topic 3: dbt Core Concepts | - Models and materializations
|
| Topic 4: Analytics Engineering Foundations | - SQL proficiency for analytics
|
| Topic 5: Deployment and Orchestration | - Running dbt in production
|
dbt Labs dbt Analytics Engineering Certification FAQ: Straight Answers Before You Study
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The dbt-Analytics-Engineering exam gives you 120 minutes to work through Not publicly disclosed (commonly reported ~60–70 questions) questions. That pace punishes perfectionism — the candidates who struggle are usually the ones who burned ten minutes on one stubborn item. Train the skill directly: take timed, full-length mocks in the ExamPrepAway desktop or online test engine, practice skipping and returning, and walk in with a pacing strategy you have already tested.
No formal prerequisites required; strong SQL and data modeling knowledge recommended
Vendor requirements do change, so before you commit to an exam date, confirm the latest eligibility rules on the official dbt Labs exam page.
The dbt Labs dbt Analytics Engineering Certification syllabus is split into 5 domains, anchored by Testing and Data Quality, dbt Core Concepts, and Deployment and Orchestration. The full breakdown sits in the syllabus section above — treat it as the checklist that decides where your study hours go.
The passing mark for the dbt-Analytics-Engineering exam is Not publicly disclosed, and the official registration fee is $100 USD (varies by region). Remember that the fee covers a single attempt — fail, and the retake costs the same again with no loyalty discount. Protect the budget by self-testing first: drill 359 practice questions at ExamPrepAway under timed conditions until your scores clear the bar with margin, then register.
The dbt-Analytics-Engineering exam, officially the dbt Analytics Engineering Certification Exam, is dbt Labs's benchmark for proving you can work with its technologies in professional settings; passing it awards the dbt Analytics Engineering Certification certification, which sits at the Professional level. Because the credential is issued by the vendor itself, it carries weight with employers that self-assessed skills simply cannot match.
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Registration for the dbt Labs dbt Analytics Engineering Certification runs through the official channels below:
Worth knowing before you book: the dbt-Analytics-Engineering exam is delivered Online proctored exam (via dbt Labs certification platform / authorized proctoring provider).
dbt Labs dbt Analytics Engineering Certification Sample Questions:
(Multiple Select)
- A. Writing custom macros to encapsulate frequently used SQL logic.
- B. Using SELECT * to include all columns dynamically
- C. Nesting multiple correlated subqueries within a single query_
- D. Leveraging window functions to calculate values across rows or groups.
Correct Answer: B,C 🗳️
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Logic Error
- A. Adjust the model's SQL query and re-run it to observe the changes in output.
- B. Deploy the modified model to a development or staging environment.
- C. Create a separate branch specifically for addressing this issue.
- D. Write new unit tests that specifically target the faulty calculation logic with various input scenarios.
Correct Answer: A 🗳️
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You're onboarding a new dbt project to an existing production database. You find tables that might conflict with naming conventions used by dbt. What's a proactive way to handle this?
- A. Update your dbt models to use aliases that avoid the name collisions.
- B. Manually rename the conflicting tables in the database prior to running any dbt jobs.
- C. Add configuration settings to your database connection profile instructing dbt to use a specific schema prefix.
- D. Customize the schema property in your dbt_project.yml to namespace dbt-generated objects.
Correct Answer: D 🗳️
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A dbt deployment pipeline fails on the production environment, yet everything works as intended in development and staging. Which areas would you investigate when troubleshooting?
- A. Check if all required permissions are granted to the dbt user on the production database.
- B. Verify the credentials and connection details between production and other environments in your profiles-yml.
- C. Ensure consistency of dependency versions across different environments-
- D. Consider schema differences, if any between the production data warehouse and other environments.
Correct Answer: C 🗳️
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You implement dbt tests to monitor data quality over time. Which of the following scenarios could these tests potentially help you detect?
- A. Gradual degradation in source data completeness due to an API rate limit being reached
- B. An unexpected spike in web traffic leading to invalid data entries in your source system-
- C. An accidental schema change by someone working outside of your dbt project-
- D. Performance issues in your data warehouse impacting the speed of dbt model updates.
Correct Answer: A,B,C 🗳️
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