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Drag and Drop Question
You have a Microsoft Foundry project that contains a customer support agent grounded in internal documentation.
After a recent update, users report the following issues:
- Some answers are unsupported by retrieved documents.
- A small number of responses are flagged for policy violations.
You need to evaluate each issue.
Which observability signals should you use for each issue? To answer, drag the appropriate observability signals to the correct issues. Each observability signal may be used once, more than once, or not at all. You may need to drag the spit bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?

• A. Configure an Azure AI Search connection in Project1 and reference the connection in each application.
• B. Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.
• C. Call Azure AI Search directly from each application by using Microsoft Entra authentication.
• D. Enable a managed identity for each application and call Azure AI Search directly.

You have an Azure AI Search indexer that ingests PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?

• A. GenAI Prompt
• B. Document Extraction
• C. Azure Content Understanding in Foundry Tools
• D. Document Layout

You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?

• A. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
• B. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
• C. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
• D. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores

You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?

• A. vector search only
• B. hybrid search
• C. semantic search only
• D. keyword search only