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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q355-Q360):

NEW QUESTION # 355
A team member proposes directly joining multiple raw source tables into a single large model for convenience.
Which of the following is a potential long-term consequence of this approach?

A. Incremental materialization strategies would no longer be applicable.
B. Your DAG might inadvertently develop circular dependencies.
C. The dbt test command might run slower due to the complex join logic.
D. Maintenance complexity increases as business logic or source tables change.

Answer: D

Explanation:
Directly joining raw sources sacrifices modularity and flexibility. While option A is partially true, B highlights a more significant maintainability risk.

NEW QUESTION # 356
Logic Error

A. Write new unit tests that specifically target the faulty calculation logic with various input scenarios.
B. Deploy the modified model to a development or staging environment.
C. Create a separate branch specifically for addressing this issue.
D. Adjust the model's SQL query and re-run it to observe the changes in output.

Answer: D

Explanation:
Branching creates an isolated workspace, unit tests proactively define expected results, then you modify the code, and finally, you deploy to a non-production environment for further testing.

NEW QUESTION # 357
Which of the following scenarios would typically warrant the use of production data in a development environment?

A. Development environments should generally avoid using real production data to minimize risks.
B. Testing changes to a critical business reporting model for accuracy before deployment.
C. Training new members of your team on writing SQL and basic dbt models.
D. Debugging a logic error that only manifests itself under the exact conditions of real-world production data.

Answer: A

Explanation:
While A and C might occasionally have valid use cases for production data in development, it usually should be avoided. Consider these for special circumstances:A Ideally, realistic test data or anonymized production samples should be used. C: Production data snapshots or masked/anonymized versions might be necessary in rare situations.

NEW QUESTION # 358
(Multiple Select)

A. dbt will issue a warning but continue processing.
B. Downstream models relying on the staging model will continue to function.
C. The staging model run will fail.
D. The dbt run will succeed, but the staging model will have null values due to the missing data.

Answer: C,D

Explanation:
A and C are correct. A missing dependency will cause SQL errors, and nulls would likely occur where the dropped table's data was used. B is incorrect. Downstream models will often fail if an upstream model fails. D is incorrect. dbt's strict dependency handling is likely to stop the process and throw errors.

NEW QUESTION # 359
Your project has models calculating order metrics. Some models aggregate data daily; others do monthly roll-ups. What's a likely performance issue if all models are materialized as tables?

A. Conflicts might arise due to models overwriting each other's data.
B. Unnecessary computations, as monthly models would rebuild daily data redundantly.
C. Testing would become impractical due to increased runtimes.
D. The DAG visualization would be too complex to understand.

Answer: B

Explanation:
Rebuilding pre-aggregated data is inefficient. While testing becomes slower (D), the primary concern is computational waste.

NEW QUESTION # 360
......

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