最新dbt Labs認證dbt-Analytics-Engineering考試考題

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implementing dbt Tests10%- Custom tests
- Test configuration and execution
- Built-in tests
Topic 2: Managing Data Pipelines15%- CI/CD integration
- Pipeline orchestration
- Deployment strategies
Topic 3: dbt Models Governance15%- Version control integration
- Naming conventions and standards
- Project organization and structure
Topic 4: Debugging and Error Resolution15%- Resolving data quality issues
- Debugging techniques
- Identifying modeling errors
Topic 5: Leveraging dbt State5%- State-aware operations
- State management
Topic 6: Creating and Maintaining Documentation10%- Descriptions and metadata
- Generating documentation
- Documentation standards
Topic 7: External Dependencies10%- Managing snapshots
- Using packages
- External sources integration
Topic 8: Developing dbt Models20%- Model design and structure
  • 1. Materialization types
  • 2. Ref and source functions
  • 3. Incremental models
- Model optimization
  • 1. Model contracts
  • 2. Performance tuning

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最新的 Analytics Engineers dbt-Analytics-Engineering 免費考試真題 (Q103-Q108):

問題 #103
You frequently need to debug model failures mid-DAG during development. Which of the following dbt features would be MOST helpful in this scenario?

答案：C

解題說明：
--fail-fast instructs dbt to stop processing as soon as a model fails, making it great for debugging a chain without processing everything every time.


問題 #104
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs.

答案：

解題說明：

Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.


問題 #105
Your dbt project contains models that process data with varying levels of sensitivity. Which of the following might be reasons to utilize different schemas or even different targets within your data warehouse for these data sensitivity levels?

答案：D

解題說明：
A: Separate schemas or targets enhance access control granularity. B: Some data workloads might have different performance requirements- C: Compliance often necessitates structured data organization-


問題 #106
Your team decides to strictly enforce a linear history on the main branch. How does this decision impact your development and merge workflow?

答案：A

解題說明：
A: Rebasing is required to keep history linear during merges- B: Merging a rebased branch would typically result in a single commit C: Code reviews need to adapt, as granular commit history on the feature branch won't be reflected in the main branch.


問題 #107
dbt Model Snippet:SQL

答案：C

解題說明：
Subqueries can be performance bottlenecks- The other parts, while potentially optimizable, are less likely to cause a dramatic slowdown.


問題 #108
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