AI-103模擬資料100％パス率, Developing AI Apps and Agents on Azure

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Microsoft AI-103 Exam Syllabus Topics:

Section	Weight	Objectives
Plan and manage Azure AI solutions	25–30%	- Manage AI solution development lifecycle 1. Monitor and maintain AI workloads 2. Configure model and agent deployments 3. Integrate with CI/CD pipelines - Design Azure AI infrastructure 1. Select appropriate Azure AI Foundry services 2. Plan for security, compliance, and responsible AI 3. Design for scalability, availability, and cost optimization
Implement generative AI and agentic solutions	30–35%	- Build generative AI applications 1. Build retrieval-augmented generation (RAG) solutions 2. Implement prompt engineering and optimization 3. Integrate Azure OpenAI and other models 4. Implement function calling and tool use - Design and implement intelligent agents 1. Implement multi-agent workflows and orchestration 2. Manage state, memory, and context 3. Integrate agents with external systems and data sources 4. Select agent architecture patterns
Implement text and speech analysis solutions	10–15%	- Implement natural language processing 1. Perform sentiment analysis, entity recognition, and summarization 2. Build conversational language understanding 3. Use Azure AI Language services - Implement speech capabilities 1. Speech-to-text and text-to-speech integration 2. Speech translation and speaker recognition
Implement information extraction and knowledge mining	10–15%	- Build knowledge bases and search solutions 1. Design knowledge mining pipelines 2. Implement Azure AI Search 3. Create and manage vector indexes - Extract structured data from documents 1. Process forms, invoices, and unstructured content 2. Use Azure AI Document Intelligence
Implement computer vision solutions	10–15%	- Implement image analysis and processing 1. Use Azure AI Vision services 2. Extract text and structure from images 3. Implement object detection and image classification - Build multimodal solutions 1. Combine vision and language capabilities 2. Process and analyze video content

>> AI-103模擬資料 <<

AI-103勉強の資料 & AI-103テスト模擬問題集

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Microsoft Developing AI Apps and Agents on Azure 認定 AI-103 試験問題 (Q42-Q47):

質問 # 42
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={"required"}
B. tool_choice={"type":"knowledge_base"}
C. tool_choice={"auto"}
D. tool_choice ={"type":"mcp"}

正解：A

解説：
To deterministically force the agent to invoke your Model Context Protocol (MCP) tool on every run, you must pass tool_choice="required" into the run_create_and_process method.
The 'required' tool choice: Setting this parameter to 'required' forces the underlying Azure OpenAI model to invoke one of your available tools on every response, ensuring the agent doesn't guess answers from the base model.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/tool-best-practice

質問 # 43
You have a Microsoft Foundry project that contains an agent and uses a GitHub repository. The repository contains a YAM file named File1 that defines the evaluation settings of the agent. You need to create a GitHub Actions workflow that runs the evaluation defined in File1 when a pull request (PR) is opened. How should you configure the workflow?

A. Set evaluation-config to the path of the YAML file.
B. Set model-deployment-name to the deployed model.
C. Set tenant-id to the Microsoft Entra tenant ID
D. Set project-endpoint to the endpoint of the project.

正解：D

解説：
The correct configuration choice is to set project-endpoint to the endpoint of the project.
azure-ai-project-endpoint: This parameter is required and must be set to the URL endpoint of your Microsoft Foundry project. This aligns with setting project-endpoint.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluation-github-action

質問 # 44
You need to improve classification accuracy by showing the model how correct classifications look, without retraining the model or storing knowledge permanently.

A. zero-shot learning
B. few-shot learning
C. Retrieval Augmented Generation (RAG)
D. chain of thought

正解：B

解説：
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Few-shot learning , more precisely few-shot prompting, supplies a small number of representative input
/output examples as part of the prompt. Those examples demonstrate the intended classification behavior before the model receives the new support ticket.
For example, the prompt might show one ticket classified as Billing, one as AccountAccess, and one as TechnicalIssue. The model can infer the expected labels, style, and output structure from the demonstrations and then apply the pattern to the incoming ticket.
The model ' s weights are not updated. The examples exist only in the prompt context for that invocation or conversation, so the approach satisfies both requirements: no retraining and no permanent knowledge storage.
Zero-shot prompting would supply only task instructions and no demonstrations. RAG retrieves external source information to ground model responses; it is not primarily a mechanism for showing examples of desired classification outputs. Chain-of-thought relates to reasoning behavior rather than labeled demonstration examples.
Few-shot prompting is therefore the direct method for increasing task accuracy by showing the model what correct results look like.
Study Guide alignment: Optimize generative AI systems through prompt engineering and adjust model behavior without retraining . Microsoft Learn

質問 # 45
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?

A. Modify the system message instructions.
B. Apply top-p sampling.
C. Increase the value of the temperature parameter.
D. Add few-shot examples.

正解：A

解説：
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that the business requirement is for Agent1 to answer questions only about the products sold by Contoso . This is a behavioral boundary for the agent, so it should be implemented in the highest-priority instructions that define the agent's role, allowed scope, and refusal behavior.
Microsoft Foundry guidance states that a system message is used to steer model behavior, define the assistant' s role and boundaries, and add safety or quality constraints for the scenario. The system message should instruct Agent1 to answer only when the question concerns Contoso products, use the configured Contoso product documentation as grounding, and politely refuse or redirect questions about non-Contoso products.
Top-p sampling and temperature control randomness, not business-domain scope. Increasing temperature would make responses less deterministic. Few-shot examples can support desired behavior, but examples alone are weaker than explicit system-level instructions for defining operating boundaries. Reference topics:
system message design, prompt engineering, agent instructions, response constraints, and grounded generative AI behavior.

質問 # 46
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
- Each workflow run must include a retrieval step before generating a
response.
- Tool calls must authenticate by using the published agent's own
identity.
- Tool access must use an identity isolated from other project
resources.
- Tool access must use support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

質問 # 47
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