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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
| External Dependencies | 10% | - Managing snapshots - External sources integration - Using packages |
| dbt Models Governance | 15% | - Project organization and structure - Naming conventions and standards - Version control integration |
| Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Developing dbt Models | 20% | - Model design and structure
|
| Debugging and Error Resolution | 15% | - Identifying modeling errors - Resolving data quality issues - Debugging techniques |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Implementing dbt Tests | 10% | - Custom tests - Test configuration and execution - Built-in tests |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question 1
A project stakeholder prefers a text-based lineage representation over the graphical DAG. Which of these approaches might be a suitable alternative?
A. Utilize a macro that generates a Markdown list representation of model dependencies.
B. Disable the DAG visualization entirely within the generated documentation.
C. Extend the dbt documentation framework to create a custom text-based lineage view
D. Rely on third-party tools that specialize in parsing dbt projects to produce textual lineage outputs
Question 2
You want to write a custom test to check for potential anomalies in a time-series dataset. Which of the following would be the MOST important considerations when designing this test?
A. Defining thresholds for what constitutes an anomaly in your business context.
B. Ensuring the test integrates seamlessly with dbt's docs feature.
C. Understanding the expected seasonality or patterns in the data.
D. Identifying the metrics (e.g., average, standard deviation) relevant to your data.
Question 3
Your development environment uses a smaller, sampled dataset compared to production for efficiency. When a new performance-sensitive model is deployed, users report significant slowdowns. What might be some of the potential causes?
A. Performance optimizations made in development might not translate directly to the larger production data volumes.
B. Your data warehouse might have different resource configurations or indexing in the production environment.
C. The smaller dataset in development didn't reveal performance issues that become apparent at the scale of production data
D. All of the above-
Question 4
Your team decides to strictly enforce a linear history on the main branch. How does this decision impact your development and merge workflow?
A. All of the above.
B. Pull requests would always show a single commit upon merging with the main branch.
C. Code reviews might need to focus on the overall change in the pull request instead of individual commits.
D. Feature branches would likely need to utilize git rebase before merging.
Question 5
You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.
A. Create a seed file to load the data directly into a table.
B. Utilize a dbt package tailored for the specific API, if available.
C. Write a dbt macro to encapsulate the API interaction logic.
D. Create a source with a loaded_at_field and write custom logic to snapshot API data.
Solutions:
| Question 1 Answer: A,C,D | Question 2 Answer: A,C,D | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: B |
