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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Topic 1: 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)
Topic 2: 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 3: Testing and Data Quality	- Data validation strategies 1. Schema testing practices 2. CI-based validation workflows - Built-in and custom tests 1. Generic tests (unique, not null, relationships) 2. Custom SQL tests
Topic 4: dbt Core Concepts	- Project structure and configuration 1. dbt_project.yml configuration 2. Packages and dependencies - Models and materializations 1. Views, tables, incremental models 2. Ref and source functions
Topic 5: 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. Data modeling in SQL 2. Joins, aggregations, and window functions

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Both B and C.
B. Permanently remove the sensitive file from Git history using tools like git filter-branch.
C. Contact the repository hosting service for assistance.
D. Use git reset -hard to revert the commit and remove the file locally.

Answer: B,C

Explanation:
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

NEW QUESTION # 109
(Multiple Select)

A. Encapsulating complex logic in well-named macros with descriptive comments.
B. Preferring long, monolithic SQL files with minimal CTEs.
C. Using clear and consistent naming conventions across models and macros.
D. Minimizing explicit model dependencies to create loosely coupled components.

Answer: A,C

Explanation:
Well-documented macros and consistent naming boost readability. Large monolithic SQL files hinder maintainability.

NEW QUESTION # 110
After implementing thorough dbt tests, you confidently deploy changes. Yet, downstream Bl dashboards break, and stakeholders complain about inaccurate dat a. What could have gone wrong?

A. Your tests were correct, but a bug in dbt prevented them from running successfully.
B. The tests you wrote were too strict, causing false positives.
C. Critical assumptions were made about the data in the Bl layer, independent of dbt models.
D. There are logic errors in your dbt models that the tests didn't catch.

Answer: C,D

Explanation:
This emphasizes that models can be internally correct yet still produce unusable output if the source data changes. Tests only cover what they're designed for; the Bl layer may have its own data quality requirements.

NEW QUESTION # 111
(Multiple Select)

A. Macros for fiscal period calculations or string manipulations.
B. A custom adapter to connect to a niche data source.
C. Models addressing a domain-specific use case (e.g., subscription analytics).
D. Snapshots that track the state of your sources on a schedule.

Answer: A,C

Explanation:
Packages excel at providing reusable macros and domain-specific model sets. Snapshots and adapters are usually independent of or extend the core dbt feature set.

NEW QUESTION # 112
You want to use a YAML anchor to reuse a standard block of descriptive text within multiple models. How would you implement this?

A. Define a Jinja macro for the text block, and then call the macro within your descriptions.
B. YAML anchors are not directly supported in the context of dbt model and source descriptions-
C. Insert a YAML anchor (&) in your dbt_projectyml file and reference it in your model files.
D. Create a YAML anchor within the relevant model file where the text should be reused.

Answer: D

Explanation:
YAML anchors (&) and aliases (*) allow you to define reusable blocks of text within the same YAML file, such as model files or sources.yml.

NEW QUESTION # 113
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