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dbt Labs dbt-Analytics-Engineering Exam Overview:

Certification Vendor:dbt Labs
Exam Name:dbt Analytics Engineering Certification Exam
Exam Number:dbt-Analytics-Engineering
Exam Format:Online proctored, Multiple choice
Related Certifications:dbt Architect Certification
Exam Price:200 USD
Real Exam Qty:65
Passing Score:65%
Exam Duration:120 minutes
Certificate Validity Period:2 years
Available Languages:English
Recommended Training:dbt Certification Study Guide
dbt Official Documentation
Exam Registration:Official Registration Page
Sample Questions:dbt Labs dbt-Analytics-Engineering Sample Questions
Exam Way:Online, remotely proctored; delivered via Caveon testing platform
Pre Condition:SQL proficiency recommended; 6+ months practical experience with dbt Core or dbt Cloud suggested; no mandatory prerequisites
Official Syllabus URL:https://www.getdbt.com/certifications/analytics-engineer-certification-exam

dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

dbt Labs dbt Analytics Engineering Certification Sample Questions:

Question #1

You want to standardize descriptions across a large number of models that share the same underlying source schemas. Which approach might be the most efficient?

  • A. Manually copy and paste the descriptions into each model's YAML file.
  • B. Create a CSV lookup file and maintain custom source descriptions within the file.
  • C. Focus on using a version control system and enforce descriptions directly within the source system.
  • D. Write a Jinja macro that dynamically generates descriptions based on metadata.
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Correct Answer: D  🗳️

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Question #2

You're collaborating with a third-party analytics vendor who requires access to a subset of processed data within your dbt project for integration into their platform. How might you approach this securely?

  • A. Generate a dedicated readout schema in your data warehouse with appropriate grants for the vendor's user
  • B. Utilize a dbt package to export specific models to CSV or JSON format and provide those files to the vendor
  • C. A combination of the above might be needed, depending on the vendor's integration capabilities-
  • D. Set up a data sharing mechanism through your data warehouse, if it supports this functionality, enabling controlled access.
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Correct Answer: C  🗳️

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Question #3

You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

  • A. The tests will fail, preventing the changed data from being loaded, preserving the old behavior.
  • B. Your dbt models may silently produce incorrect results due to the changed distribution.
  • C. dbt will automatically adjust your tests to accommodate the new distribution.
  • D. The changed data distribution has no impact on downstream dbt models.
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Correct Answer: B  🗳️

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Question #4

"The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

  • A. Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
  • B. All of the above.
  • C. Test the model in isolation using the dbt run -select flag to narrow down dependencies.
  • D. Examine the source tables for data quality issues like duplicates or missing values.
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Correct Answer: B  🗳️

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Question #5

You define a source table and include an override:YAML

  • A. The overrides property needs to be defined at the source level, not the table level.
  • B. The overrides property is not valid for tables within your dbt_project.yml.
  • C. The value 'my_project' is not a recognized keyword by dbt.
  • D. You need to create a macro named 'my_project' that contains the specific overriding logic.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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