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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: dbt Models Governance15%- Project organization and structure
- Naming conventions and standards
- Version control integration
Topic 2: Creating and Maintaining Documentation10%- Documentation standards
- Generating documentation
- Descriptions and metadata
Topic 3: Debugging and Error Resolution15%- Identifying modeling errors
- Resolving data quality issues
- Debugging techniques
Topic 4: Leveraging dbt State5%- State management
- State-aware operations
Topic 5: Implementing dbt Tests10%- Test configuration and execution
- Built-in tests
- Custom tests
Topic 6: External Dependencies10%- External sources integration
- Using packages
- Managing snapshots
Topic 7: Managing Data Pipelines15%- Pipeline orchestration
- Deployment strategies
- CI/CD integration
Topic 8: Developing dbt Models20%- Model design and structure
  • 1. Materialization types
  • 2. Incremental models
  • 3. Ref and source functions
- Model optimization
  • 1. Model contracts
  • 2. Performance tuning

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You're onboarding a new team member and need to set them up with a personal sandbox environment within your existing project. What factors do you need to consider?

A) How to provision data warehouse resources if needed, such as a separate schema or potentially even a dedicated database.
B) Whether they need access to production data replicas and how to control this securely
C) How to configure a profile on their machine and guide them to isolate changes to their sandbox.
D) All of the above.


2. (Multiple Select)

A) Running dbt tests in parallel with dbt model builds whenever possible-
B) Configuring tests to run only on models that have changed or have upstream changes.
C) Scheduling less critical tests to run during off-peak hours.
D) Designing tests, where feasible, to examine subsets of data rather than full tables.


3. You frequently need to implement custom SQL tests that are very similar but with slight variations in parameters. Which dbt feature could streamline this process?

A) The relationships test
B) dbt seeds
C) dbt snapshots
D) dbt macros


4. You're evaluating whether to adopt an external tool that offers a visual, drag-and-drop interface for building data models. When considering this alongside dbt, what's a KEY factor to weigh?

A) Whether the tool can handle the full complexity of your data transformations.
B) The ability of the tool to generate dbt-compatible SQL and integrate into your version control workflow.
C) The ease of migrating existing dbt projects into the visual tool.
D) If the visual tool has advanced machine learning capabilities for data cleaning.


5. Unexpected Results

A) Use diff tools or version control history to compare the refactored logic against the previous version.
B) Revert the changes in your version control system to restore the model to its previous working state.
C) Implement targeted profiling to measure the execution time of specific query sections within the model.


Solutions:

Question # 1
Answer: D
Question # 2
Answer: B,C,D
Question # 3
Answer: D
Question # 4
Answer: A,B
Question # 5
Answer: B

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