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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Fundamentals	15%	- dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure
Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Git version control integration - Environments (dev, staging, prod) - CI/CD with dbt Cloud
Data Transformation Techniques	25%	- Refactoring and incremental models - Macros and packages - Jinja templating - Common table expressions and subqueries
Models	25%	- Sources and references - Writing and managing SQL models - Materializations (table, view, ephemeral, incremental) - Snapshots - Seeds
Testing and Documentation	20%	- Documentation generation - dbt docs and DAG visualization - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships)

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Faster dbt model compilation and execution times.
B. Improved lineage and data traceability within the documentation.
C. Better data quality due to the automated enforcement of column descriptions
D. Increased discoverability for other team members working on the project

Answer: A

Explanation:
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

NEW QUESTION # 83
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?

A. Version control system metadata (e.g., Git history)
B. Specialized data observability platforms
C. Custom logging built into your models
D. dbt Cloud's built-in job monitoring features, if applicable

Answer: B,D

Explanation:
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.

NEW QUESTION # 84
(Multiple Select)

A. The cost-efficiency of the development environment solution.
B. The need for feature parity (functions, syntax) with the production warehouse.
C. The overall size and complexity of your dbt project
D. The technology used for your production data warehouse.

Answer: A,B,D

Explanation:
A ensures compatibility and consistent developer experience. B avoids excessive development costs. C prevents surprises when porting code- D influences the scale of resources you need in development.

NEW QUESTION # 85
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?

A. All of the above-
B. Performance optimizations made in development might not translate directly to the larger production data volumes.
C. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data
D. Your data warehouse might have different resource configurations or indexing in the production environment.

Answer: A

Explanation:
A: Sampled data obscures performance problems related to data volume. B: Optimizations designed for small data might not be effective at scale. C: Differences between environments can drastically affect performance.

NEW QUESTION # 86
"Our dbt jobs take hours to run, and it's hard to know why!" says a frustrated colleague. What initial investigation steps would you suggest?

A. Review any 'on-run-start' or 'on-run-end' hooks that might add unexpected overhead.
B. Check your dbt_project.yml configuration for any project-wide performance settings to optimize.
C. All of the above.
D. Look into dbt's profiling capabilities to pinpoint the slowest models and queries.

Answer: A

Explanation:
Each of these could provide valuable insights. Start with profiling to find the most performance-critical areas, but don't neglect hooks and potential config-level optimizations.

NEW QUESTION # 87
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