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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Models	25%	- Sources and references - Seeds - Writing and managing SQL models - Snapshots - Materializations (table, view, ephemeral, incremental)
Topic 2: dbt Fundamentals	15%	- dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud
Topic 3: Data Transformation Techniques	25%	- Common table expressions and subqueries - Refactoring and incremental models - Macros and packages - Jinja templating
Topic 4: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - CI/CD with dbt Cloud - Git version control integration - Environments (dev, staging, prod)
Topic 5: Testing and Documentation	20%	- dbt docs and DAG visualization - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
You're integrating a new dbt package and find its models conflict with naming conventions in your existing project. Which approach generally provides the most flexible solution?

A. Override the package's macros and redefine them within your project.
B. Manually edit the SQL within the conflicting package models.
C. Utilize the dbt_project.yml file to re-alias the models provided by the package.
D. Contact the package maintainers to request changes aligning with your conventions.

Answer: C

Explanation:
Re-aliasing at the project level offers a centralized and maintainable way to manage naming conflicts. Editing package code directly should be avoided if possible.

NEW QUESTION # 222
(Multiple Select)

A. Macros can completely obfuscate dependencies, making them untraceable.
B. If macros themselves have dependencies on other macros, this adds an extra layer to consider.
C. If macros reference raw tables, those dependencies might not be reflected in dbt's lineage graphs.
D. Macros generally make dependency tracking easier by encapsulating logic.

Answer: B,C

Explanation:
Macros can introduce "hidden" dependencies. Also, if your macros build upon each other, you must track their internal dependencies as well.

NEW QUESTION # 223
(Multiple Select)

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

Answer: C,D

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 # 224
(Multiple Select)

A. Your production database has stricter data type constraints than development.
B. Environment variables used for sensitive credentials are not set correctly in production.
C. Your development profile in dbt_projectyml has incorrect connection details.
D. You have accidentally granted write permissions to the production data source during testing.

Answer: A,B

Explanation:
Explanation: A could reveal issues only present when interacting with the real production data. C is a common source of environment-specific errors. B would likely cause errors everywhere. D is a serious issue, but wouldn't cause a regular dbt run to fail differently.

NEW QUESTION # 225
You want to configure dbt to prevent tests from running if one or more of their parent models is unselected.
Which test command should you execute?
Choose 1 option.

A. dbt test --select "orders" --indirect-selection=buildable
B. dbt test --select "orders"
C. dbt test --select "orders" --indirect-selection=cautious
D. dbt test --select "orders" --indirect-selection=empty

Answer: D

NEW QUESTION # 226
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