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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
| Topic 2: Developing dbt Models | 20% | - Model design and structure
|
| Topic 3: Managing Data Pipelines | 15% | - Pipeline orchestration - CI/CD integration - Deployment strategies |
| Topic 4: Leveraging dbt State | 5% | - State management - State-aware operations |
| Topic 5: dbt Models Governance | 15% | - Version control integration - Project organization and structure - Naming conventions and standards |
| Topic 6: Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors |
| Topic 7: External Dependencies | 10% | - Using packages - Managing snapshots - External sources integration |
| Topic 8: Implementing dbt Tests | 10% | - Built-in tests - Test configuration and execution - Custom tests |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?
A) Utilize the 'defer' option to allow upstream models to run while awaiting the source update.
B) Set up an alert to notify users when the upstream data source is not refreshed on time.
C) Employ a snapshot of the third-party source to create a historical record.
D) Configure a 'view' that gracefully handles missing data from the third-party source.
2. (Multiple Select)
A) Indenting freshness incorrectly under the customers table.
B) The error_after freshness check syntax.
C) Misspelling "orders" as "orderes".
D) Using an 's' at the end of tables.
3. You introduce a new test, which unexpectedly leads to many models previously deemed 'successful' now failing.
What should your initial troubleshooting steps include?
A) Check if the test is revealing previously overlooked data quality problems.
B) Review the test's severity configuration (warning vs_ error).
C) Temporarily raise the test's failure threshold to give you time to investigate.
D) Verify the test logic doesn't contain errors.
4. (Multiple Select)
A) Implement a custom data test to specifically check the column for null values.
B) Examine the source data for the model to verify the presence of expected values.
C) Switch the model materialization from 'view' to 'table'.
D) Add intermediate CTEs within the model's SQL to inspect data at different transformation stages.
5. Which of the following scenarios would typically warrant the use of production data in a development environment?
A) Development environments should generally avoid using real production data to minimize risks.
B) Testing changes to a critical business reporting model for accuracy before deployment.
C) Training new members of your team on writing SQL and basic dbt models.
D) Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C | Question # 3 Answer: A,B,D | Question # 4 Answer: B,D | Question # 5 Answer: A |
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