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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Creating and Maintaining Documentation | 10% | - Documentation standards - Generating documentation - Descriptions and metadata |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors |
| Developing dbt Models | 20% | - Model optimization
|
| Managing Data Pipelines | 15% | - CI/CD integration - Deployment strategies - Pipeline orchestration |
| Leveraging dbt State | 5% | - State management - State-aware operations |
| External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| dbt Models Governance | 15% | - Project organization and structure - Naming conventions and standards - Version control integration |
| Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You notice a pattern where warnings are generated for several models due to missing descriptions. How could you configure dbt_project.yml to fail these models instead when descriptions are absent?
A) Set a project-level severity setting under the tests: section.
B) Introduce a custom macro to validate model descriptions_
C) Globally change the default warning behavior using the warn-if: configuration
D) Add on-run-start and on-run-end hooks to perform custom checks
2. Data Quality Issue
A) Add specific schema tests to the model, asserting that the column in question should not contain nulls.
B) Run dbt test on the model and see if existing tests fail, indicating a possible regression.
C) Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
D) Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.
3. (Multiple Select)
A) Preferring long, monolithic SQL files with minimal CTEs.
B) Minimizing explicit model dependencies to create loosely coupled components.
C) Encapsulating complex logic in well-named macros with descriptive comments.
D) Using clear and consistent naming conventions across models and macros.
4. You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?
A) Close the original pull request and create a new branch with the revised implementation.
B) Request that your collaborators edit the code directly within the pull request interface.
C) Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.
D) Directly modify the commits on your existing feature branch using git rebase -i.
5. While working on a feature branch, you need to incorporate some critical bug fixes that were merged into the main branch. Which Git commands, or combination of commands, would you use?
A) git merge main
B) git checkout main followed by git pull
C) Both B and C are viable options.
D) git rebase main
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,B,C,D | Question # 3 Answer: C,D | Question # 4 Answer: A | Question # 5 Answer: A,D |
