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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: Testing and Data Quality	- Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships) - Data validation strategies 1. CI-based validation workflows 2. Schema testing practices
Topic 2: Documentation and Lineage	- Data lineage understanding 1. Dependency tracking with ref() 2. Directed acyclic graph (DAG) - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Topic 3: Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Role of dbt in analytics engineering 2. Warehouse-centric transformation workflows
Topic 4: Deployment and Orchestration	- Running dbt in production 1. CI/CD integration patterns 2. dbt Cloud and job scheduling - Environments and workflows 1. Version control with Git 2. Development vs production environments
Topic 5: dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
You're onboarding a new team member and need to set them up with a personal sandbox environment within your existing project. What factors do you need to consider?

A. How to provision data warehouse resources if needed, such as a separate schema or potentially even a dedicated database.
B. All of the above.
C. Whether they need access to production data replicas and how to control this securely
D. How to configure a profile on their machine and guide them to isolate changes to their sandbox.

Answer: B

Explanation:
A: Resource allocation impacts security and costs. B. Sandbox access should align with the developer's role and data privacy requirements. C: Setting up the developer's machine and workflow is essential-

NEW QUESTION # 300
(Multiple Select)

A. Your dbt tests are invalid.
B. An upstream source table schema was updated without updating the dbt model.
C. You made a typo when defining the column name in your model.
D. A ref function referencing another model is incorrect.

Answer: B,C,D

Explanation:
All but option D could lead to this specific error. Tests wouldn't directly cause a column mismatch, but they might help you catch the error.

NEW QUESTION # 301
A user reports a critical security issue discovered in a third-party dbt package you use. Where could you most likely find updated information about potential vulnerabilities or fixes?

A. Directly from the maintainer of the dbt package, often in a GitHub repository or similar
B. The locally generated dbt documentation for the installed packages.
C. Within dbt Cloud's central package documentation and release notes.
D. The packages.yml file in your dbt project.

Answer: A

Explanation:
Package maintainers are the primary source of truth- However, options like A and C might provide additional resources:A. Your packages.yml might help identify the package C: dbt Cloud might aggregate some vulnerability information.

NEW QUESTION # 302
Your team follows a convention where commit messages include a relevant issue tracker reference (e.g., JIRA- 123). You've been working on multiple features and fixes. Which of the following best promotes clean commits?

A. Break down your changes into smaller, atomic commits, each referencing the issue it addresses.
B. Commit all your changes at once with a generic message like "Project updates."
C. Include all relevant issue references in a single commit message when you're ready to create a pull request.
D. Don't worry about commit messages, as the pull request title and description are more important.

Answer: A

Explanation:
Atomic commits focus on a single change, making history easier to understand. Referencing relevant issues creates a link between code changes and project tasks.

NEW QUESTION # 303
In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
B. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
C. When performance is a top priority, and the overhead of additional data transformations is undesirable.
D. In environments where strict data governance and transformation processes are in place.

Answer: A

Explanation:
Raw data is often unpredictable- However, under specific conditions like those listed in A, B, or C, it could be considered if the risks and trade-offs are well understood.

NEW QUESTION # 304
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