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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q304-Q309):

NEW QUESTION # 304
A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

Answer: C

Explanation:
Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.


NEW QUESTION # 305
A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:

Answer: A

Explanation:
A: Intermediate data might be unrefined and not suitable for decision-making. B: Stakeholders might misinterpret the context of intermediary data. C: It's vital to guide stakeholders to final data outputs designed for analysis.


NEW QUESTION # 306
You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

Answer: B,C

Explanation:
A and B touch on the crucial issues of how such a tool fits into your established workflow with dbt as the orchestrator, and whether it really matches the sophistication of your needs.


NEW QUESTION # 307
(Multiple Select)

Answer: C,D

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 # 308
(Multiple Select)

Answer: A,B

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.


NEW QUESTION # 309
......

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