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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Debugging and Error Resolution | 15% | - Identifying modeling errors - Resolving data quality issues - Debugging techniques |
| Managing Data Pipelines | 15% | - CI/CD integration - Deployment strategies - Pipeline orchestration |
| Developing dbt Models | 20% | - Model optimization
|
| Implementing dbt Tests | 10% | - Built-in tests - Custom tests - Test configuration and execution |
| Creating and Maintaining Documentation | 10% | - Generating documentation - Documentation standards - Descriptions and metadata |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You're tasked with adding functionality to your dbt project for:
A) Handling slowly changing dimensions.
B) Auditing data quality with comprehensive anomaly detection tests.
C) Calculating complex financial metrics specific to your industry
2. "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) Test the model in isolation using the dbt run -select flag to narrow down dependencies.
B) Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
C) Examine the source tables for data quality issues like duplicates or missing values.
D) All of the above.
3. When integrating a new data source, you notice many existing models could benefit from it. What's the most maintainable way to incorporate the new data within a DRY approach?
A) Build a snapshot of the new data combined with relevant data from existing models.
B) Add the new data source directly to each relevant model's SQL query.
C) Create a base model that incorporates the new data source and use the ref() function in downstream models.
D) Establish a naming convention for the new data elements to avoid hardcoding them within model SQL.
4. (Multiple Select)
A) You're trying to optimize the performance of a particularly complex transformation.
B) Data type conversions in your models consistently fail during execution.
C) You suspect that filters in a source definition arent being applied correctly.
D) You encounter an error message that references a specific line number in the compiled code.
5. A model refactors a frequently used SQL calculation. Traditionally this logic was copied across multiple models. Which of the following is the LEAST likely benefit of refactoring this calculation into a macro?
A) Faster dbt job execution time
B) Improved readability of models
C) Simplified dependency tracking
D) Reduced error potential due to a single source of logic
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A,C | Question # 5 Answer: A |
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