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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Models	25%	- Seeds - Sources and references - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Snapshots
Testing and Documentation	20%	- Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests - Documentation generation - dbt docs and DAG visualization
Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod)
dbt Fundamentals	15%	- dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure
Data Transformation Techniques	25%	- 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 (Q309-Q314):

NEW QUESTION # 309
Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?

A. Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.
B. Write dbt macros that use temporary tables to simulate larger datasets during development
C. Selectively snapshot a subset of production data into your development environment.
D. Run a separate dbt project against production data for testing, but target a staging schema.

Answer: A,C,D

Explanation:
A directly addresses the resource limitation, if possible. C offers a controlled way to bring in real data. D creates a sandboxed environment within production. B is clever, but unlikely to be truly representative.

NEW QUESTION # 310
While setting up profiles.yml, a colleague suggests hardcoding database passwords directly. You propose alternative approaches for the sake of security. Which of the following are valid arguments in favor of your approach?

A. Hardcoded credentials can be accidentally committed to version control, posing a security risk.
B. Credentials often need to be rotated, and hardcoding them would make updates cumbersome.
C. All of the above.
D. Using environment variables or secrets managers improves maintainability and auditability of credentials.

Answer: C

Explanation:
A: Version control should never contain sensitive data. B. Security practices often necessitate credential rotation. C: Centralizing credentials allows for more secure and controlled management

NEW QUESTION # 311
A new feature you're developing requires significant changes across multiple models in your dbt project. Which branching strategy would be the most suitable and why?

A. Create separate branches for each model you'll be modifying to minimize conflicts.
B. Avoid using branches altogether, as they can introduce unnecessary complexity into a dbt workflow
C. Create a feature branch for the new development to isolate your work.
D. Work directly on the main branch, as it ensures your changes are always up-to-date.

Answer: C

Explanation:
Feature branches provide a dedicated workspace allowing you to work on changes without impacting the stability of the main branch. This is vital for larger changesets.

NEW QUESTION # 312
You need to roll out a database migration mid-month. It requires changes to some of your dbt model schemas. How would you manage a smooth transition?

A. Deploy model changes alongside database changes, accepting potential errors for a period.
B. Use feature branches and environment-aware logic in dbt to allow pre- and post-migration versions to coexist temporarily.
C. Duplicate models, modifying them for the new schema, and switch over in your job configuration.
D. Pause all dbt jobs until after the database migration is fully completed.

Answer: B

Explanation:
C allows for gradual rollout and controlled testing. A' would lead to downtime, B potentially too much downtime, and D increases maintenance overhead.

NEW QUESTION # 313
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?

A. Set up pre-commit hooks within the project file to enforce linting and formatting rules.
B. Define custom macros in the project file to encapsulate commonly used SQL patterns.
C. Configure project-level tests to ensure adherence to data quality standards.
D. Include a docs: section with detailed descriptions of core models and transformations.

Answer: C

Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.

NEW QUESTION # 314
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