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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Leveraging dbt State	5%	- State-aware operations - State management
Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests
Creating and Maintaining Documentation	10%	- Documentation standards - Generating documentation - Descriptions and metadata
Developing dbt Models	20%	- Model design and structure 1. Ref and source functions 2. Incremental models 3. Materialization types - Model optimization 1. Model contracts 2. Performance tuning
Managing Data Pipelines	15%	- Pipeline orchestration - Deployment strategies - CI/CD integration
dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Debugging and Error Resolution	15%	- Debugging techniques - Identifying modeling errors - Resolving data quality issues
External Dependencies	10%	- Managing snapshots - External sources integration - Using packages

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.

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

Answer: D

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 # 61
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.

A. model_a, model_c, model_d, model_e, model_f
B. model_a, model_c, model_e
C. model_a
D. model_a, model_c

Answer: D

Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.

NEW QUESTION # 62
dbt Model Snippet

A. The logic contained within a custom macro.
B. The underlying data types of o.customer_id and c.customer_id mismatch.
C. The presence of filters on the source fct_orders or dim_customers.
D. The ref() functions arent correctly resolving dependencies.

Answer: C

Explanation:
Filters on sources frequently cause missing rows in joins. Check your source definitions and any model- level filtering. While the others could be issues, start with the most common cause.

NEW QUESTION # 63
You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?

A. Your test case is accidentally permitting null values, which bypass the check.
B. The model's data type isn't compatible with the accepted_values test.
C. A downstream process is overwriting the results after the model is built.
D. The accepted_values test configuration has the wrong column specified.

Answer: A,B,D

Explanation:
Each of these directly impacts the accepted_values test's effectiveness, while D is unlikely for a singular test.

NEW QUESTION # 64
You try updating a column description using its corresponding YAML file. After regenerating the docs, the update doesn't appear. Which of the following might be the cause?

A. Your browser is aggressively caching the documentation site.
B. The column description might need additional configuration with a test for it to render in the docs.
C. Changes in column descriptions only update the database schema, not the project documentation.
D. There could be a syntax error within the updated description that dbt is silently ignoring.

Answer: A,D

Explanation:
A: Aggressive browser caching can sometimes prevent updates from being reflected. C: Even small syntax errors in YAML can lead to dbt ignoring certain elements during the documentation generation process.

NEW QUESTION # 65
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