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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.

A. .yml configurations for sources and refs are parsed for dependency information.
B. Information is gathered from the use of source and ref macros.
C. The underlying SQL code is parsed and relationships are created from explicit table references.
D. All source and ref macros are resolved to database objects and dbt queries them for dependencies.

Answer: B

Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.

NEW QUESTION # 230
(Multiple Select)

A. Catching subtle errors or discrepancies that may not cause immediate functional issues.
B. Testing performance under a simulated heavy load.
C. Ensuring data synchronization after a migration.
D. Validating changes during development before deploying a new dbt model version.

Answer: A,C,D

Explanation:
These tests help ensure alignment between the source of truth and the analytics environment. D (performance testing) is a different type of test.

NEW QUESTION # 231
(Multiple Select)

A. You encounter an error message that references a specific line number in the compiled code.
B. Data type conversions in your models consistently fail during execution.
C. You suspect that filters in a source definition arent being applied correctly.
D. You're trying to optimize the performance of a particularly complex transformation.

Answer: C,D

Explanation:
Compiled SQL helps verify applied logic and is useful for query optimization. Line numbers in errors often point to compiled SQL- Data type errors are typically less related to the compiled structure.

NEW QUESTION # 232
You want to experiment with a new column in an existing model without risking production changes. Which configuration strategy in your dbt_project.yml would support this safely?

A. Add a -select flag when using dbt run to execute only the modified model.
B. Define separate configurations for the column under the model and use the enabled property.
C. Modify the model SQL using the config.get() macro to dynamically control the column's behavior
D. Utilize git branches and have different dbt_project.yml files for your production and experimental branches.

Answer: B

Explanation:
Model configurations with the enabled property offer a structured way to experiment within the project file.

NEW QUESTION # 233
You need to run a custom Python script after your dbt models execute. Where and how can you configure this in your dbt_project.yml file?

A. Include the script as a 'seed' file with a specific execution order
B. Create an 'integration test' and use the PythonFunction class-
C. Define it as a 'snapshot' to ensure it runs after all models.
D. Utilize the on-run-end hook configuration.

Answer: D

Explanation:
on-run-end hooks allow you to execute arbitrary code at the end of a dbt run.

NEW QUESTION # 234
......

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