AB-100: Agentic AI Business Solutions Architect › Practice Assessment for Exam AB-100: Agentic AI Business Solutions Architect › Question #26661
Single Choice Moderate

Q

A company uses Azure OpenAI On Your Data in Microsoft Foundry and an Azure AI Search index. The assistant has inScope=true configured and must include citations.

Telemetry shows that the assistant sometimes reports that information is unavailable, even though the content exists in the indexed documents.

You confirm that relevant chunks are retrieved but not included in the model prompt.

You need to increase the likelihood that retrieved chunks are included in the prompt by changing only inferencing parameters.

What should you do?

ID: #26661 Practice Assessment for Exam AB-100: Agentic AI Business Solutions Architect 4 views
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#26661Q ID
ModerateDifficulty
Practice Assessment for Exam AB-100: Agentic AI Business Solutions ArchitectTopic
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  • A Reduce the strictness parameter. ✔ ✖
  • B Change the embedding model used during ingestion. ✔ ✖
  • C Switch the search query type to vectorSemanticHybrid. ✔ ✖
  • D Recreate the Azure AI Search index with smaller chunks. ✔ ✖
Correct Answer

Explanation

Objective:

3.1 Analyze, monitor, and tune AI-powered business solutions

What This Item Tests:

Apply AI-based tools to analyze and identify issues and perform tuning

Additional Reading:

Troubleshooting and best practices for Azure OpenAI On Your Data (classic)

Rationale:

The strictness parameter controls how aggressively retrieved content is filtered before being included in the prompt sent to the model. Reducing strictness increases recall and enables more relevant chunks to pass through without modifying documents, embeddings, or the search index. Changing embeddings or chunking requires re-ingestion, and query type affects retrieval rather than post-retrieval filtering.

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