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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Debugging and Error Resolution	15%	- Identifying modeling errors - Debugging techniques - Resolving data quality issues
External Dependencies	10%	- Using packages - Managing snapshots - External sources integration
Leveraging dbt State	5%	- State-aware operations - State management
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%	- Deployment strategies - Pipeline orchestration - CI/CD integration
dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
Implementing dbt Tests	10%	- Built-in tests - Custom tests - Test configuration and execution
Creating and Maintaining Documentation	10%	- Documentation standards - Descriptions and metadata - Generating documentation

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dbt-Analytics-Engineering Practice Questions & dbt-Analytics-Engineering Actual Lab Questions: dbt Analytics Engineering Certification Exam

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
Which of the following scenarios might warrant a git rebase instead of a standard git merge when integrating your changes into the main branch?

A. Your branch has diverged significantly from the main branch with unrelated changes.
B. You want to avoid a merge commit and keep a linear project history.
C. You need to resolve complex merge conflicts that arise during a standard merge.
D. You only want to include a subset of your feature branch commits in the main branch.

Answer: A,B

Explanation:
A Rebasing creates a more linear history, making it easier to trace changes over time. D. Rebasing allows for a cleaner integration when a feature branch contains intermingled changes that shouldn't all be merged.

NEW QUESTION # 119
You're debugging a runtime error. Analyzing the compiled SQL alone doesn't provide enough clues to find the root cause. What's your next debugging step?

A. Examine the dbt logs for more context about the model's execution and dependencies.
B. Modify the compiled SQL directly and re-run the query against your warehouse.
C. Disable custom macros to see if one of them is interfering-
D. Compare compiled SQL from a previous successful run to the current failing version-

Answer: A

Explanation:
Logs often provide additional error messages and context Comparing SQL versions can be helpful but might not illuminate the root cause. Never modify compiled SQL directly

NEW QUESTION # 120
A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:

A. Use detailed commit messages to explain the rationale behind each set of related changes.
B. Resubmit the pull request as several smaller, more focused pull requests.
C. All of the above.
D. Provide a comprehensive summary of the intent and scope of the changes within the pull request description.

Answer: D

Explanation:
A: Smaller PRs allow for more focused and effective reviews. B: Clear commit messages help understand the step-by-step changes within the PR. C: A summary provides a bird's-eye view for the reviewer.

NEW QUESTION # 121
You want to restrict which models can refer to a specific model.
How can this be done?
Choose 1 option.

A. Add model groups and set access to private.
B. Modify your data warehouse's permissions to restrict usage of the object.
C. Create another version with a _v2 suffix and set the latest version to 2 in the model's configuration.
D. Set access to protected for that specific model.

Answer: A

Explanation:
The correct answer is A: Add model groups and set access to private.
dbt's access control system is designed to restrict which models may reference others purely within the dbt project itself, independent of data warehouse permissions. This system uses model groups and an access property, which can be set to public, protected, or private. Setting a model to private means that only models within the same group are allowed to reference it. Any attempts to reference the model from outside the group will cause a compilation error, enforcing strong boundaries between domains or data products within a dbt project.
Option C (protected) is incorrect because protected allows references from other groups but requires explicit configuration-the intention is stewardship, not restriction. It does not fully restrict referencing.
Option B is incorrect because dbt model access control is not tied to warehouse permissions. Warehouse- level grants cannot control dependency references inside the dbt DAG.
Option D is irrelevant to access control. Versioning does not restrict references; it only manages updates to published models.
Therefore, the only correct way to restrict which models can reference another model is by placing it into a model group and marking it as private, which enforces strict internal usage boundaries.

NEW QUESTION # 122
"Isn't debugging harder with many smaller models in a DAG?" How would you address this concern expressed by a colleague?

A. "Debugging is slightly harder, but the benefits outweigh the added effort."
B. "Our DAG will be simple, so debugging model dependencies won't be a concern."
C. "We'll rely extensively on snapshots to trace errors back to their origin."
D. "Smaller models are easier to test individually, leading to faster issue identification."

Answer: D

Explanation:
Emphasize how modularity isolates problems and enables targeted testing. While the other options have some truth, option B presents the most convincing argument.

NEW QUESTION # 123
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