Microsoft Developing AI Apps and Agents on Azure : AI-103 Exam

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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?

A. run success rate
B. token usage
C. evaluation metrics
D. latency

Question 2

You have an Azure subscription that contains an Azure OpenAI resource.
You deploy the GPT-4 model to the resource.
You need to ensure that you can upload files that will be used as grounding data for the model.
Which two types of resources should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Azure Blob Storage
B. Azure AI Document Intelligence
C. Azure AI Search
D. Azure SQL
E. Azure AI Bot Service

Question 3

You have a Microsoft Foundry project that contains a support-ticket triage agent built by using the Foundry Agent Service.
The agent uses tool to classify the ticket type and sot the ticket priority.
Sometimes, the same support case continues across multiple sessions over several days.
You need to persist state by using a durable ID to ensure that the agent can automatically reuse the full interaction history. The solution must preserve previous user messages, tool calls and tool outputs across turns and sessions.
Which runtime component should you use?

A. conversation
B. agent
C. output item
D. response

Question 4

You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search.
Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations.
You are provided with the following code snippet that runs the agent.

You need to add the correct tool _choiceparameter to the code to deterministically force the agent to invoke the MCP tool on each run.
What should you add?

A. tool_choice ={"type":"mcp"}
B. tool_choice={"type":"knowledge_base"}
C. tool_choice={"auto"}
D. tool_choice={"required"}

Question 5

Hotspot Question
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: