Last Updated: Jun 03, 2026
No. of Questions: 77 Questions & Answers with Testing Engine
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1. Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
2. An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?
A) Archive the model version.
B) Unregister the model version.
C) Archive the training dataset for the model version.
D) Delete the model version.
3. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?
A) Add a data validation component in pipeline
B) Ignore schema differences
C) Use larger compute
D) Retrain manually when failure occurs
4. Hotspot Question
You manage a Retrieval-Augmented Generation (RAG) system that retrieves internal policy documents from a vector index.
Recent analysis shows that:
- Retrieved results frequently include duplicated content from the same document.
- Retrieved chunks sometimes span unrelated policy sections.
You review the following retrieval and ingestion configurations:
You need to reduce duplicated retrieval results and improve chunk relevance across policy sections. For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
5. You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?
A) Same user identity
B) Same dataset name
C) Versioned datasets and environments
D) Same compute SKU
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: Only visible for members | Question # 5 Answer: C |
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