Microsoft Developing AI Apps and Agents on Azure : AI-103
考試編碼: AI-103
考試名稱: Developing AI Apps and Agents on Azure
更新時間: 2026-10-11
問題數量: 159 題
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電子檔(PDF)試用關於Azure AI Engineer Associate Developing AI Apps and Agents on Azure考古題
擁有三種最流行的Developing AI Apps and Agents on Azure題庫版本
我們的Developing AI Apps and Agents on Azure題庫一共分為三個版本；
PDF版本：這個版本的特點在於“方便閱讀,支持打印”,對於不適應使用電腦而更喜歡紙質版的Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫客戶而言,這是一個不錯的選擇,讓您有更真實的觸感,重回學生時代,找回高考時拼命做題的感覺。與其他兩個版本Developing AI Apps and Agents on Azure題庫相比,PDF版本更方便攜帶,讓您走到哪兒題目做到哪兒;
APP線上版本：Developing AI Apps and Agents on Azure線上版本的最大好處就是不限使用設備,支持任何電子設備,同時還支持離線使用,只要你的電子設備是有電的,就可以隨時隨地的刷題啦。在第一次聯網的情況下打開Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫,之後可以不用聯網也能刷題。這是目前最方便的一個版本。不同職業,不同需求的客戶可以根據自身情況來購買最為適合自己的的組合,找到一種最適合的做題方式,更為有效的利用自己的時間,早日取得Developing AI Apps and Agents on Azure證書。
安全保障的付款方式
Developing AI Apps and Agents on Azure使用我們目前國際最大最值得信賴的付款方式,只有在最為安全的支付環境下,買家才能夠放心的付款購買Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫,並且利益才能有保障。現在因為隱私洩露的問題比較嚴重,有很多的客戶擔心自己的隱私被洩露出去,但是請各位放心,所有購買我們Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫產品的客戶信息都是完全保密的。
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最後是售後問題,為了保障到客戶的基本利益,我們的客服是7/24小時在線支持,不管Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫產品在任何時間有任何問題,您都可以立刻聯繫我們的客服,我們會以最快的速度為您處理好,盡量不影響您的正常使用。當然,如果您覺得我們的Azure AI Engineer Associate Developing AI Apps and Agents on Azure-AI-103題庫產品有什麼不足之處,或者是建議,您都可以聯繫我們的客戶,我們都將進行改進,爭取更為優質的為客戶服務。
購買後，立即下載 AI-103 題库 (Developing AI Apps and Agents on Azure): 成功付款後, 我們的體統將自動通過電子郵箱將你已購買的產品發送到你的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查你的垃圾郵件。）
Microsoft AI-103 考試大綱主題:
| 章節 | 權重 | 目標 |
|---|---|---|
| 建構生成式AI解決方案 | 25-30% | - 開發生成式AI應用程式
|
| 建構電腦視覺解決方案 | 10-15% | - 分析視覺內容
|
| 建構代理服務解決方案 | 20-25% | - 管理代理服務運作程序
|
| 建構文字分析與資訊擷取解決方案 | 10-15% | - 分析並擷取資訊
|
| 規劃與管理Azure AI解決方案 | 25-30% | - 規劃Azure AI資源
|
最新的 Azure AI Engineer Associate AI-103 免費考試真題：
問題 #1
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses a stored access key to retrieve secrets from an Azure key vault, which violates a keyless-credentials requirement.
You need to ensure that the agent can retrieve the secrets. The solution must follow the principle of least privilege.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
問題 #2
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
A. an agent
B. a plugin
C. a kernel
D. a function
問題 #3
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
Hotspot Question
You need to ensure that Agent1Dev Team can access Agent1. The solution must meet the security and compliance requirements.
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.
問題 #4
Drag and Drop Question
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.
問題 #5
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?
A. Speech recognition requests will return a 4xx error until a new custom model is deployed.
B. Speech recognition requests will continue to use the expired custom model until the model is removed manually.
C. Speech recognition requests will fall back to the most recent base model for the same locale.
D. The custom model will be deleted automatically when the model expires.
問題與答案：
| 問題 #1 答案： 僅成員可見 | 問題 #2 答案： B | 問題 #3 答案： 僅成員可見 | 問題 #4 答案： 僅成員可見 | 問題 #5 答案： C |
|
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