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Microsoft AI-103 Exam Prep
Developing AI Apps and Agents on Azure (Page 9 )

Updated On: 3-Oct-2026
Page 7 of 39
PDF with 187 Questions

You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
-▪ Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI
Search.
▪ Require deep, multi-step reasoning across long contexts.
▪ Generate detailed natural language responses.
Which type of model should you use to power the agent?

  1. a multimodal model
  2. a small language model (SLM)
  3. a key phrase extraction model
  4. a large language model (LLM)

Answer(s): D

Explanation:

For a support agent requiring deep multi-step reasoning, long context processing, and strict grounding in internal documents, the GPT-5.5 (or GPT-5.5-Pro) model from the Azure AI Foundry model catalog is the best choice.
Here is how GPT-5.5 directly addresses the requirements:
Deep Multi-Step Reasoning: These frontier models use deliberate reasoning and iterative planning before generating a response. This allows the agent to decompose complex support queries, analyze alternatives,
and prevent hallucinations without requiring heavy custom prompt engineering.
Grounded, Accurate Responses:
Rather than doing this alone, pair the model with Foundry IQ connected to your Azure AI Search indices.
Foundry IQ's agentic retrieval engine will pull exactly the right context, allowing GPT-5.5 to synthesize the answer and cite the original documentation.
Long Context Handling:
GPT-5.5 models support massive context windows, allowing them to ingest extensive previous conversational turns alongside detailed internal documentation in a single pass without losing track of important rules.
Note:
GPT-5.5 is a large language model (LLM).While it is a flagship LLM built on OpenAI's advanced transformer architecture, it also features natively omnimodal capabilities that allow it to process both text and images seamlessly within a single unified framework. However, when choosing between the specific categories provided, its primary core classification is a Large Language Model (LLM).
Key Details About GPT-5.5
Core Architecture: Large Language Model (LLM) built by OpenAI.
Primary Focus: Highly optimized for complex reasoning, multi-step problem solving, coding, and autonomous agentic workflows.
Input/Output Capabilities: Supports text and image inputs with text-based outputs.


Reference:

https://developers.openai.com/api/docs/guides/reasoning



DRAG DROP (Drag and Drop is not supported)
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Note: Each correct selection is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:





Box 1: "tool_choice"
To fix this issue and ensure that the agent always calls a tool during execution, you need to configure the tool_choice parameter in the run_payload.
Box 2: "required"
In Microsoft Foundry's Agent Service (similar to the underlying Azure OpenAI / Assistants API architecture),
setting "tool_choice": "required" forces the model to select and execute one or more tools before returning its response.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-best-practice



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?

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

Answer(s): B

Explanation:

To meet your security and architecture requirements, you must add the Azure AI Search instance as a
Connection within your Azure AI Foundry project and configure Managed Identities for role-based access control (RBAC).
To securely unify your search configuration without API keys, add the Azure AI Search instance as a shared
Connection in your Azure AI Foundry project, disable key authentication on the search service, and authorize your applications using Azure RBAC and Managed Identities.
Note:
*-> 1. Create a Project Connection
Connect Azure AI Search directly inside the Azure AI Foundry hub or project.
*-> Share the same search service configuration across all connected client applications automatically.
Centralize your search endpoint details to reduce administrative overhead.
2. Disable Key Authentication
3. Enable Managed Identities
4. Update the Web App Code


Reference:

https://learn.microsoft.com/en-us/azure/foundry-classic/tutorials/copilot-sdk-create-resources



DRAG DROP (Drag and Drop is not supported)
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
-▪ Ensure that the agent can perform calculations during conversations.
▪ Ensure that the agent can access up-to-date information from public websites.
▪ Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Note: Each correct selection is worth one point.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:






Box 1: Grounding with Bing Search
Ensure that the agent can access up-to-date information from public websites.
You should use Grounding with Bing Search to optimize your Microsoft Foundry agent workflow.
Large Language Models (LLMs) suffer from fixed knowledge cutoffs and cannot natively access real-time information. Integrating Grounding with Bing Search enables your agent to autonomously fetch up-to-date,
public web data, synthesize facts, and respond with exact inline URL citations.
Box 2: Code interpreter
Ensure that the agent can perform calculations during conversations.
To fulfill this requirement in a Microsoft Foundry Agent Service project, you should use Agent Tools (specifically
Function Calling or a Code Interpreter tool).
In the Microsoft Agent Framework and Foundry Agent Service, tools are the native mechanism used to extend an agent's reasoning capabilities beyond text generation, allowing them to access real-time data and perform exact mathematical computations during a conversation.
Box 3: File Search
Ensure that the agent can retrieve information from documents uploaded directly to the agent.
To give your Microsoft AI Foundry agent the capability to directly reference and retrieve information from uploaded documents, you should enable and configure the File Search tool (via the Foundry Agent Service).
This tool functions alongside the agent's system prompts to perform Retrieval-Augmented Generation (RAG)
directly on your data.


Reference:

https://learn.microsoft.com/en-us/azure/foundry-classic/agents/how-to/tools-classic/bing-code-samples https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/runtime-components https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-catalog



You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
Note: Each correct selection is worth one point.

  1. a Log Analytics workspace
  2. Application Insights
  3. OpenTelemetry
  4. the Azure Monitor Agent
  5. Microsoft Sentinel

Answer(s): B,C

Explanation:

To implement end-to-end tracing for a Microsoft Foundry prompt agent invoked from an external Python service, you should use a combination of Azure Application Insights, the Azure AI Projects SDK, and
OpenTelemetry.
Core Components
Azure Application Insights: This serves as the centralized telemetry store. You must connect an Application
Insights resource to your Foundry project via the Tracing tab in the Foundry portal to enable ingestion.
Azure AI Projects SDK (azure-ai-projects): Used in your Python service to create an AIProjectClient, which authenticates and communicates with the Foundry agent.
OpenTelemetry Python SDK: Foundry tracing is built on the OpenTelemetry standard. You will need opentelemetry-sdk and azure-monitor-opentelemetry to instrument your external service.


Reference:

https://learn.microsoft.com/en-us/azure/foundry/observability/how-to/trace-agent-setup



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