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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implementing dbt Tests	10%	- Built-in tests - Test configuration and execution - Custom tests
Topic 2: Creating and Maintaining Documentation	10%	- Descriptions and metadata - Documentation standards - Generating documentation
Topic 3: dbt Models Governance	15%	- Version control integration - Project organization and structure - Naming conventions and standards
Topic 4: Managing Data Pipelines	15%	- Deployment strategies - Pipeline orchestration - CI/CD integration
Topic 5: Leveraging dbt State	5%	- State management - State-aware operations
Topic 6: Developing dbt Models	20%	- Model design and structure 1. Incremental models 2. Ref and source functions 3. Materialization types - Model optimization 1. Model contracts 2. Performance tuning
Topic 7: Debugging and Error Resolution	15%	- Resolving data quality issues - Debugging techniques - Identifying modeling errors
Topic 8: External Dependencies	10%	- External sources integration - Using packages - Managing snapshots

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q102-Q107):

NEW QUESTION # 102
13. An analyst on your team has informed you that the business logic creating the is_active column of your stg_users model is incorrect.
You update the column logic to:
case
when state = 'Active'
then true
else false
end as is_active
Which test can you add on the state column to support your expectations of the source data? Choose 1 option.

A. - name: state
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null
B. - name: state
tests:
- not_null
- unique
C. - name: is_active
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null
D. - name: is_active
tests:
- not_null
- unique

Answer: A

Explanation:
The purpose of this question is to determine how to validate that the input values in the state column support the business logic that determines the is_active field. Since the logic checks whether state = 'Active', it is critical that the state column only contains values that the business process expects. In the example shown, acceptable states appear to be: 'active', 'churned', and 'trial'.
The correct way to enforce this expectation is to apply an accepted_values test on the state column. This ensures that any unexpected state (e.g., 'inactive', 'pending', 'deleted', or NULL) will cause the test to fail, alerting the team that the upstream system is producing unexpected or invalid values. Additionally, adding not_null ensures every user record contains a valid state.
Option A is the only configuration that applies the accepted values test to the correct column (state) and reflects the expected domain of values.
Options B and D incorrectly apply tests to the derived column is_active, not the source column that needs validation. Option C only checks nullability and uniqueness, which does not validate the range of allowed values and thus does not protect the business logic.
Therefore, Option A is the only correct answer.

NEW QUESTION # 103
(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 # 104
You introduce a set of schema tests on source tables. These tests run successfully, but upon investigation, you find subtle data quality issues are still being missed. What are potential reasons for this?

A. Schema tests only check basic structure and not the specific content of the data.
B. There's a bug in dbt's schema test implementation.
C. Schema tests only run once per day by default.
D. Schema tests shouldn't be relied on for comprehensive data quality assurance.

Answer: A,D

Explanation:
Schema tests are a good first line of defense but don't replace the need for more nuanced checks that examine the data's actual content.

NEW QUESTION # 105
External Dependency

A. Introduce a temporary staging model to transform the data into a compatible structure for your existing models.
B. Implement schema tests for the affected models to catch similar breakages immediately in the future.
C. Work with stakeholders to understand if the external change is intentional and adjust the business logic if needed.
D. All of the above.

Answer: D

Explanation:
This requires adaptation, proactive future detection, and possible requirements adjustment since you can't fix what's not in your control.

NEW QUESTION # 106
Which two code snippets result in a lineage line being shown in the DAG? Choose 2 options.

A. {{ target.schema }}
B. {{ source('jaffle_shop.customers') }}
C. {{ ref('stg_jaffle_shop__customers') }}
D. {{ source('source', 'table') }}
E. dbt_jsmith.stg_jaffle_shop__customers

Answer: C,D

Explanation:
In dbt, the Directed Acyclic Graph (DAG) is created from model dependencies, which dbt identifies only through two mechanisms: the ref() function for referencing dbt models and the source() function for referencing declared sources. These functions are explicitly designed to tell dbt how models relate to each other so that dbt can track lineage, enforce build order, and generate documentation.
Option A, {{ source('source', 'table') }}, correctly references a declared source. Any model using source() establishes an upstream dependency on that source table. dbt will therefore draw a lineage line in the DAG from the source to the model.
Option B, {{ ref('stg_jaffle_shop__customers') }}, is the canonical way to reference another dbt model. ref() ensures dbt determines dependencies at compile time, and thus creates a DAG edge between this model and its parent.
The remaining options do not create lineage:
* C directly references a database object, which dbt cannot interpret as a dependency.
* D uses a Jinja variable unrelated to lineage.
* E is invalid, because source() requires two arguments: a source name and table name, not a dotted string.
Therefore, A and B are the only valid answers.

NEW QUESTION # 107
......

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