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[General] New dbt Labs dbt-Analytics-Engineering Study Notes | dbt-Analytics-Engineering L

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【General】 New dbt Labs dbt-Analytics-Engineering Study Notes | dbt-Analytics-Engineering L

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q296-Q301):NEW QUESTION # 296
you're working with a dbt project where a central model fct_orders is refactored. Several downstream models rely on this fact table. Which of the following is the MOST efficient way to determine which downstream models might be affected by this change?
  • A. Utilize the dbt docs generate and dbt docs serve commands.
  • B. Execute dbt run and observe for errors.
  • C. Use the dbt Is command to generate a lineage graph.
  • D. Manually review the code of all downstream models.
  • E. Run dbt test to see which tests might fail.
Answer: A
Explanation:
While options B, D, and E can indirectly hint at broken models, they're not the most efficient methods. The dbt docs commands allow you to directly visualize and explore dependencies within your dbt project, making it the ideal choice for this scenario.

NEW QUESTION # 297
You're setting up a new dbt project on a recently provisioned data warehouse. What actions are essential before your initial development begins?
  • A. Establishing access controls and user roles specific to the dbt project's requirements.
  • B. Configuring monitoring and alerting to track data warehouse usage and events related to dbt jobs
  • C. All of the above
  • D. Defining and implementing a schema and naming conventions for your dbt models within the data warehouse.
Answer: C
Explanation:
A: Organization is key for maintainability and collaboration. B: Secure access is fundamental from the outset C: Visibility into job execution and resource usage is crucial.

NEW QUESTION # 298
You introduce a minor formatting change to a large SQL model. Afterwards, the results change unexpectedly. Which dbt-related issue might explain this, even if the SQL itself appears correct?
  • A. An upstream data issue only surfaces due to the formatting change making that part of the query execute differently.
  • B. You've used a reserved keyword as a column alias without properly quoting it.
  • C. There's a version mismatch between the dbt library used during development and in production.
  • D. A subtle indentation change in a CTE accidentally modifies the execution order within the query.
Answer: D
Explanation:
Whitespace matters in SQL, and CTE order is often significant even if the query might still be validly formatted.

NEW QUESTION # 299
While working on a pull request, you realize a much better approach exists. Which actions could you take?
  • A. Make the changes on your branch and use git push -force to overwrite the existing remote branch.
  • B. Close the original pull request, create a new branch, and open a new pull request.
  • C. Avoid introducing major changes during a pull request, as it hinders effective code review.
  • D. Add new commits to the existing branch, explaining the change of direction in the comments on the pull request.
Answer: B,D
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 # 300
Type Mismatch
  • A. Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
  • B. Introduce a staging model specifically to perform the type conversion, creating a clean separation.
  • C. Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
  • D. Handle the type conversion with a custom macro and apply it selectively within your dbt project.
Answer: B
Explanation:
A staging model offers the most control and flexibility. Direct source modification risks disrupting other systems. In-model CASTs work but clutter the core logic. Macros are powerful but add complexity.

NEW QUESTION # 301
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