dbt Labs dbt Analytics Engineering Certification 認定 dbt-Analytics-Engineering 試験問題:
1. You're working with ephemeral models in your dbt project. Which scenarios could cause an ephemeral model to fail?
A) A syntax error in the model's SQL code.
B) A downstream dependency of the ephemeral model fails.
C) An upstream source table is temporarily inaccessible.
D) Data quality tests defined within the model are not met.
2. You want to configure dbt to prevent tests from running if one or more of their parent models is unselected.
Which test command should you execute?
Choose 1 option.
A) dbt test --select "orders" --indirect-selection=empty
B) dbt test --select "orders" --indirect-selection=buildable
C) dbt test --select "orders"
D) dbt test --select "orders" --indirect-selection=cautious
3. You run a dbt job that heavily utilizes CT Es (Common Table Expressions). In your development environment, it works flawlessly. However, in production (a different database technology), it throws errors. Which potential causes should you investigate?
A) Your production environment has resource constraints that impact the execution of complex queries.
B) CTE performance optimizations are automatically applied in development, but not in production.
C) The production database has limitations or different syntax conventions related to CTEs.
D) This indicates a bug in dbt's cross-database compatibility layer.
4. While refactoring models, you accidentally drop a table in your development environment that mirrors production. Which inherent characteristic of development environments likely contributed to this issue?
A) Development environments often have less stringent permissions to facilitate experimentation.
B) Developers working in development environments might expect the ability to make and revert changes with fewer consequences.
C) Both A and C-
D) Development datasets might be incomplete or outdated compared to productiom
5. You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?
A) Create a macro, encapsulating the date calculations, and reference it in the affected models.
B) Write a custom Python function and import it into your dbt project.
C) Introduce a new intermediate table to store the result of the date calculations.
D) Continue copying the date calculation logic into each relevant model.
質問と回答：
| 質問 # 1 正解： A、C、D | 質問 # 2 正解： A | 質問 # 3 正解： A、C | 質問 # 4 正解： C | 質問 # 5 正解： A |
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