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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt-Analytics-Engineering Zertifizierung <<

dbt-Analytics-Engineering Der beste Partner bei Ihrer Vorbereitung der dbt Analytics Engineering Certification Exam

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

A. Reference an external Markdown file within the source description.
B. dbt documentation does not support multi-paragraph descriptions for sources.
C. Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
D. Write a longer description directly within the YAML file, breaking it across multiple lines.

Antwort: C

Begründung:
The block scalar syntax in YAML allows for writing multi-line text blocks that preserve the original formatting

22. Frage
You want to run and test the models, tests, and snapshots you have added or modified in development.
Which will you invoke? Choose 1 option.
Options:

A. dbt build --select state:modified --defer <path/to/artifacts>
B. dbt run --select state:modified --defer --state <path/to/artifacts>
dbt test --select state:modified --defer --state <path/to/artifacts>
C. dbt run --select state:modified --state <path/to/artifacts>
dbt test --select state:modified --state <path/to/artifacts>
D. dbt build --select state:modified --state <path/to/artifacts>
E. dbt build --select state:modified --defer --state <path/to/artifacts>

Antwort: E

Begründung:
The goal in this scenario is:
* Select only models, tests, and snapshots that were modified in development, using:state:modified
* Use already-built production objects whenever possible, so development does not need to rebuild everything. This is done with:--defer
* Compare development code against production artifacts, which requires:--state <path/to/artifacts>
* Run everything (models + tests + snapshots) in a single command. This is exactly what dbt build does.
* dbt build = run models + run tests + run snapshots + run seeds.
Putting it all together, the correct command is:
dbt build --select state:modified --defer --state <path/to/artifacts>
This single command satisfies all requirements:
* It runs only modified resources.
* It uses the production state artifacts for comparison.
* It defers to production-built models where possible.
* It runs all resource types automatically.
Why the other options are incorrect:
* A # Missing --state, so state:modified cannot function.
* B # Splits run + test separately; question asks which single command you will invoke.
* D # Missing --defer, which defeats the purpose of using production objects.
* E # Also missing --defer.
Thus, Option C is the only correct answer.

23. Frage
Which two are true about dbt tests?
Choose 2 options.

A. The full list of tests that can be applied natively can be found on dbt's package hub.
B. Tests for unique and not_null are automatically applied on the primary key of the table.
C. Tests can be built as .sql files within the /tests/ folder.
D. You can apply dbt's native tests using the constraints configuration in the model's YAML.
E. dbt ships natively with unique, not_null, relationships, and accepted_values tests.

Antwort: C,E

Begründung:
The correct answers are C and D.
dbt supports two main categories of tests: generic tests and singular tests. Generic tests are defined in YAML and applied to models or columns, while singular tests are written as SQL files placed in the /tests/ directory.
This makes Option C correct-singular tests must be created as .sql files inside that folder, and dbt will execute each file as a test query.
Option D is also correct. dbt includes four built-in generic tests: unique, not_null, relationships, and accepted_values. These are considered "native" tests and are available without requiring any additional packages. They cover the most common data quality checks and are applied through YAML configurations on models or columns.
Option A is incorrect because dbt does not automatically apply any tests. All tests-native or custom-must be explicitly defined in YAML or created manually. Nothing is inferred from primary keys.
Option B is incorrect because dbt's package hub contains community packages, not the list of native tests.
Native tests are documented directly within dbt's core functionality.
Option E is incorrect because the constraints configuration is used to create database-level constraints on supported warehouses, not to run dbt tests. Tests still require YAML test definitions or .sql files.
Thus, only C and D accurately describe dbt's testing behavior.

24. Frage
You need a very niche data transformation that no existing packages address. When should you consider building your own dbt package?

A. When strict versioning and control of the functionality are critical for your project
B. Only after you have extensively searched for potential alternative solutions.
C. If the transformation logic is complex and likely to be valuable to others in the dbt community.
D. Whenever you need to encapsulate custom SQL logic for reuse across models.

Antwort: C

Begründung:
The primary benefit of creating a custom package lies in sharing it with others. If it's solely for your project, macros or well-structured models might suffice

25. Frage
After adding several new models to your project, you want to execute only those models and their direct dependencies. Which command combination best achieves this?

A. dbt run -models +model namel +model name2 ...
B. dbt run followed by dbt test
C. dbt build
D. dbt run -exclude model namel model name2

Antwort: A

Begründung:
The + syntax with dbt run allows you to specify models and their upstream dependencies. Dbt build encompasses run and test along with snapshot execution.

26. Frage
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