Exam Name: dbt Analytics Engineering Certification Exam
Certification Provider: dbt Labs
Corresponding Certification: Analytics Engineers
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Data Pipelines | 15% | - Pipeline orchestration - CI/CD integration - Deployment strategies |
| Topic 2: Debugging and Error Resolution | 15% | - Debugging techniques - Identifying modeling errors - Resolving data quality issues |
| Topic 3: External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Topic 4: Leveraging dbt State | 5% | - State management - State-aware operations |
| Topic 5: Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation |
| Topic 6: Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
| Topic 7: dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Topic 8: Developing dbt Models | 20% | - Model design and structure
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
You maintain a large dbt project with various reporting models that source data from a set of core business tables. These core tables often include columns with sensitive information. What technique could ensure compliance and maintainability when restricting access to this data?
- A. Write complex WHERE clauses in each reporting model to exclude rows containing sensitive data.
- B. Create incremental models that directly remove the sensitive columns before reporting models utilize them.
- C. Use views to create different projections of the core tables, exposing only the necessary columns.
- D. Apply row-level security within the data warehouse itself for fine-grained control.
Correct Answer: C 🗳️
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You introduce a new data source for a dbt model. Production policies require review and approval of the source's schema before it can be used for reporting. How could you streamline this within your dbt project?
- A. Write dbt tests that explicitly fail if the new source schema does not match the approved definition.
- B. Run a separate dbt project in production exclusively for source validation, providing access to relevant stakeholders.
- C. Use dbfs ephemeral models feature to temporarily load the data for the approval process.
- D. Create a pre-hook that automates schema validation and sends the output to the designated team.
Correct Answer: B,C,D 🗳️
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A team member suggests building a dbt package for a set of reusable transformations your team frequently uses. When is this MOST likely a good idea?
- A. The transformations are complex and involve domain-specific logic that might be beneficial to others.
- B. The transformations are project-specific but used across multiple models.
- C. You need to limit access to these transformations to a specific team within your organization.
- D. The transformations are relatively simple and could be written as macros.
Correct Answer: A 🗳️
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Performance Bottleneck
- A. Introduce CTEs to pre-calculate aggregated data or common subexpressions, especially if reused repeatedly.
- B. Run EXPLAIN or equivalent on the compiled query to pinpoint the most expensive operations.
- C. Implement database-level indexes on columns involved in the joins, if appropriate.
- D. Add -full-refresh to dbt run on the model to ensure downstream models don't cause errors.
Correct Answer: A,B 🗳️
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A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?
- A. dbt docs generate --models +[model_namel
- B. It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.
- C. Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
- D. dbt docs generate -select [model_name]
Correct Answer: D 🗳️
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