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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> Exam dbt-Analytics-Engineering Duration <<

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q241-Q246):

NEW QUESTION # 241
A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:

A. Provide a comprehensive summary of the intent and scope of the changes within the pull request description.
B. Use detailed commit messages to explain the rationale behind each set of related changes.
C. Resubmit the pull request as several smaller, more focused pull requests.
D. All of the above.

Answer: A

Explanation:
A: Smaller PRs allow for more focused and effective reviews. B: Clear commit messages help understand the step-by-step changes within the PR. C: A summary provides a bird's-eye view for the reviewer.

NEW QUESTION # 242
A package you depend on has released a major update with breaking changes. How might you approach the migration?

A. Pin the package to the older version in your packages.yml file and plan a controlled migration.
B. Review the release notes and the migration guide provided by the package maintainers.
C. Update your packages.yml to point to the new version and immediately refactor all your dependent models.
D. Fork the package and maintain your own custom version indefinitely.

Answer: B

Explanation:
Understanding the changes is key' Then plan a staged migration to minimize disruption. Forking should be a last resort.

NEW QUESTION # 243
Your dbt project contains a series of interlinked models (A, B, C, D) where some models have downstream dependencies on upstream models. While running dbt run, model C fails due to a data validation test you added. Which of the following statements are potentially true?

A. Model A and Model B are unaffected and have the most up-to-date data.
B. The failure of Model C triggers an automatic rollback of Model A and Model B.
C. Model C has stale data but can still be queried.
D. Model D cannot be built until the issue with Model C is resolved.

Answer: A,C,D

Explanation:
A is true because upstream models that successfully complete are unaffected by downstream failures. B is true due to the dependency structure of the DAG. Model D cannot be created if its prerequisite, Model C, fails. C is false. dbt models don't automatically roll back upon failure unless you explicitly program this. D is true. Even though the model wasn't created successfully, the prior version (if it existed) remains in the warehouse

NEW QUESTION # 244
(Multiple Select)

A. You need to apply custom column-level transformations to a raw source.
B. You want to test different loading strategies for a large source table.
C. The source has multiple tables with related schemas requiring standardization.
D. The source data is sensitive and requires role-based access control.

Answer: A,C

Explanation:
yml files allow for complex source schemas and column transformations. Access control and loading strategies are usually handled at the database or project-level configuration.

NEW QUESTION # 245

Answer:

Explanation:

Explanation:
For models not stored under finance:
# reporter and bi
For models inside the finance folder:
# finance, reporter, bi, and public
In dbt, grants configured at the root level apply to all models unless overridden by a more specific folder- or model-level configuration.
The root-level grant is:
+grants:
+select: ['reporter', 'bi']
This means all models by default are selectable by:
* reporter
* bi
Now the finance folder contains its own override:
finance:
+grants:
+select: ['finance']
When dbt merges grants, overrides do not replace the entire list-they add onto inherited grants unless explicitly cleared. Therefore, models inside the finance folder inherit the parent grants and add the finance grant.
So models under finance are accessible to:
* finance
* reporter
* bi
* and public (implicit default in most warehouses unless denied)
Models not inside finance use only the root grants:
* reporter
* bi
Thus the correct dropdown answers are:
* reporter and bi
* finance, reporter, bi, and public

NEW QUESTION # 246
......

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