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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q277-Q282):

NEW QUESTION # 277
You get a compilation error: "Configuration key 'enabled' not found". Where in your dbt_project.yml would you start looking to fix this, and why?

A. In a seeds.yml file or the seeds section, where enabled is a valid property.
B. Within the models section, as models often use the enabled property.
C. In the top-level profiles.yml, as the enabled key is specific to setting up project profiles.
D. Under a specific source definition, as sources can also sometimes be conditionally enabled.

Answer: B

Explanation:
Since it's a generic error, start with the most common location - model configurations. The others might be valid in certain scenarios.

NEW QUESTION # 278
Type Mismatch

A. Handle the type conversion with a custom macro and apply it selectively within your dbt project.
B. Introduce a staging model specifically to perform the type conversion, creating a clean separation.
C. Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
D. Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.

Answer: B

Explanation:
A staging model offers the most control and flexibility. Direct source modification risks disrupting other systems. In-model CASTs work but clutter the core logic. Macros are powerful but add complexity.

NEW QUESTION # 279
In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A. When performance is a top priority, and the overhead of additional data transformations is undesirable.
B. In environments where strict data governance and transformation processes are in place.
C. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
D. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.

Answer: C

Explanation:
Raw data is often unpredictable- However, under specific conditions like those listed in A, B, or C, it could be considered if the risks and trade-offs are well understood.

NEW QUESTION # 280
You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

A. Directly modify the commits on your existing feature branch using git rebase -i.
B. Close the original pull request and create a new branch with the revised implementation.
C. Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
D. Request that your collaborators edit the code directly within the pull request interface.

Answer: B

Explanation:
A: A new branch often provides the cleanest history for the reworked feature. B: Altering existing commits should be used judiciously, as it 'rewrites' history and can cause problems for collaborators.

NEW QUESTION # 281
You've meticulously added descriptions and tests to your dbt models. However, after running dbt docs generate, the tests don't appear in the generated documentation. Which of the following reasons are most likely?

A. You are using a custom schema test macro that isnt configured to be included in the documentation.
B. Your tests have incorrect syntax, and dbt is silently skipping them during documentation generation.
C. There is a bug in the version of dbt you're using that specifically affects test rendering in documentation.
D. The dbt_project.yml file may not have the appropriate settings for including tests in the documentation.

Answer: A,D

Explanation:
A: Custom macros often need explicit configuration to be included in the generated documentation. C: The dbt_project.yml file controls which elements (models, tests, etc.) are included in the docs- You'll need to verify the relevant settings.

NEW QUESTION # 282
......

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