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[Hardware] dbt-Analytics-Engineering valid vce collection & dbt-Analytics-Engineering l

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【Hardware】 dbt-Analytics-Engineering valid vce collection & dbt-Analytics-Engineering l

Posted at 2/13/2026 05:15:29      View：77 | Replies：0        Print      Only Author   [Copy Link] 1#
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q273-Q278):NEW QUESTION # 273

Answer:
Explanation:

Explanation:
Update int_order_items by replacing:
* {{ source('tpch', 'orders') }} # {{ ref('stg_tpch_orders') }}
* {{ source('tpch', 'lineitem') }} # {{ ref('stg_tpch_line_items') }}
In dbt's recommended modeling pattern, sources should be referenced only in the staging layer. Downstream models (intermediate and mart/fact models) should reference staging models via ref(), not the raw source() directly. This keeps all raw-to-clean logic centralized in the staging layer and makes refactoring and documentation easier.
In the original DAG, int_order_items depends directly on tpch.orders and tpch.lineitem via source(), and staging models also depend on those same sources. That creates parallel paths from the sources and breaks the clean layered architecture.
By updating the int_order_items SQL to use:
from {{ ref('stg_tpch_orders') }}
join {{ ref('stg_tpch_line_items') }} ...
instead of:
from {{ source('tpch', 'orders') }}
join {{ source('tpch', 'lineitem') }} ...
you ensure that int_order_items sits entirely downstream of the staging layer. The new DAG becomes linear: sources # staging # intermediate (int_order_items) # any further marts. This improves modularity, reusability of cleaned logic, and makes the DAG easier to reason about and maintain.

NEW QUESTION # 274
While working on a pull request, you realize a much better approach exists. Which actions could you take?
  • A. Add new commits to the existing branch, explaining the change of direction in the comments on the pull request.
  • B. Avoid introducing major changes during a pull request, as it hinders effective code review.
  • C. Close the original pull request, create a new branch, and open a new pull request.
  • D. Make the changes on your branch and use git push -force to overwrite the existing remote branch.
Answer: A,C
Explanation:
B: Provides the cleanest history for the revised approach. C: Maintains transparency on the evolution of your thought process while allowing for review of the new changes.

NEW QUESTION # 275
You're getting inconsistent errors when adding new models in your dbt_project.yml. You suspect typos or indentation issues. How can you proactively improve this situation?
  • A. Set up a pre-commit hook that runs a YAML validation script before committing changes.
  • B. All of the above.
  • C. Write tests covering various aspects of dbt_project.yml to proactively catch errors.
  • D. Use a code editor or IDE with YAML-specific linting and auto-formatting support.
Answer: B
Explanation:
Each of these plays a role in preventing YAML errors.

NEW QUESTION # 276
External Dependency
  • A. All of the above.
  • B. Implement schema tests for the affected models to catch similar breakages immediately in the future.
  • C. Work with stakeholders to understand if the external change is intentional and adjust the business logic if needed.
  • D. Introduce a temporary staging model to transform the data into a compatible structure for your existing models.
Answer: A
Explanation:
This requires adaptation, proactive future detection, and possible requirements adjustment since you can't fix what's not in your control.

NEW QUESTION # 277
You discover a model sorting large amounts of data using an ORDER BY clause with several columns. Performance needs to be increased. What's the FIRST step in your optimization process?
  • A. Change the materialization from a 'table' to a 'view'.
  • B. Examine query execution plans to identify bottlenecks and potential index usage.
  • C. Remove unnecessary columns from the ORDER BY clause.
  • D. Add a LIMIT clause to restrict the number of records sorted.
Answer: B
Explanation:
Before implementing changes, it's crucial to understand current performance bottlenecks. Query execution plans provide this insight.

NEW QUESTION # 278
......
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