Microsoft AI-103 Study Material

Microsoft AI-103 Exam Study Material

Developing AI Apps and Agents on Azure
  • 67 Questions & Answers
  • Update Date : July 13, 2026

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Question 1

You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1. You need to ensure that App1 meets the following requirements: • Authenticates by using a Microsoft Entra managed identity • Sends prompts to a deployed model by using the Azure OpenAI Responses API 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.

Question 2

You have a customer support agent that uses the Microsoft Foundry Agent Service. Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following: • Multi-turn continuity within the session • Cross-session continuity for the same case • Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn. What should you do?

A. Persist only the final model response stored in the client application and prepend the response to future prompts. 
B. Enable memory summarization on the agent definition to persist the context automatically. 
C. Create and reuse a conversation by storing the conversation’s ID and supplying the ID on subsequent requests. 

Question 3

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? 

A. a multimodal model 
B. a key phrase extraction model 
C. a small language model (SLM) 
D. a large language model (LLM) 

Question 4

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem. After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen. You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You increase the value of the temperature parameter. Does this meet the goal? 

A. Yes 
B. No 

Question 5

You have an app named App1 that uses a Microsoft Foundry multimodal model deployment. App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context. Some uploaded images contain embedded text. You need to prevent potentially malicious instructions from being processed by the model. What should you use? 

A. protected material text 
B. prompt shields for user prompts 
C. image moderation 
D. prompt shields for documents 

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