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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
dbt Fundamentals15%- dbt project structure
- dbt Core vs dbt Cloud
- dbt workflow and best practices
Deployment and Orchestration15%- Git version control integration
- CI/CD with dbt Cloud
- Environments (dev, staging, prod)
- Jobs and scheduling in dbt Cloud
Testing and Documentation20%- Schema tests (unique, not_null, accepted_values, relationships)
- Documentation generation
- dbt docs and DAG visualization
- Custom data tests
Models25%- Sources and references
- Materializations (table, view, ephemeral, incremental)
- Writing and managing SQL models
- Seeds
- Snapshots
Data Transformation Techniques25%- Refactoring and incremental models
- Jinja templating
- Common table expressions and subqueries
- Macros and packages

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q110-Q115):

NEW QUESTION # 110
You want to trigger a dbt job immediately upon successful completion of an upstream ETL process. Which methods could achieve this?

Answer: B,D

Explanation:
A and B establish event-driven, active triggering. C is a workaround that may be inefficient and introduce a lag. D is for sequential execution, not external event dependency.


NEW QUESTION # 111
While onboarding a new team member, you need to ensure consistency between their local development setup and your existing production dbt deployment. Which of these are essential?

Answer: A

Explanation:
A: Version control and pinning dependencies ensure reliable behavior across machines. B: Accurate profiles are vital for successful dbt runs_ C: Data warehouse access is necessary for the developer to function.


NEW QUESTION # 112
(Multiple Select)

Answer: A,B

Explanation:
SELECT * can lead to performance issues and unexpected results. Correlated subqueries often have better-performing joins equivalents. Macros and window functions are good practices.


NEW QUESTION # 113
The dataset you work with contains personally identifiable information (PII). Which types of additional tooling might be necessary for your project beyond a core dbt implementation?

Answer: C,D

Explanation:
Data masking/tokenization protects sensitive data, and access governance/auditing ensures compliance when handling PII, which are distinct challenges from the core dbt workflow.


NEW QUESTION # 114
(Multiple Select)

Answer: A,C

Explanation:
Compiled SQL helps verify applied logic and is useful for query optimization. Line numbers in errors often point to compiled SQL- Data type errors are typically less related to the compiled structure.


NEW QUESTION # 115
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