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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Topic 2: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Topic 3: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 4: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 5: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
Microsoft Foundry Agent Service を使用してカスタマーサポートエージェントを作成するための計画を提案する必要があります。エージェントは以下の要件を満たす必要があります。
* 複数の会話間でユーザーの設定を保持する。
* チャット中にドキュメントを直接アップロードすることで、ユーザーが状況に応じた情報を提供できるようにする。
それぞれの要件に対して、どのFoundry機能を推奨すべきでしょうか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
To retain user preferences across conversations, use: Agent memory that uses persistent storage To enable users to provide contextual grounding during chats, use the: file search tool The correct capability for retaining user preferences is agent memory that uses persistent storage . Microsoft Foundry Agent Service memory is a managed long-term memory capability that enables continuity across sessions, devices, and workflows. It is specifically intended to let agents retain user preferences, maintain relevant historical context, and personalize responses across separate conversations. Memory stores provide the persistent storage layer, and scope can be used to segment memories for secure user-specific experiences.
The correct capability for contextual grounding from user-uploaded documents is the file search tool .
Microsoft describes file search as the tool that enables Foundry agents to search through documents and retrieve relevant information from outside the base model, including proprietary product information and user- provided documents. The file search workflow supports uploading files, creating a vector store, enabling the tool on the agent, and querying those documents through the agent.
Conversation history alone supports continuity within a conversation, but it is not durable preference memory across multiple conversations. An Azure AI Search tool is better for preconfigured enterprise indexes, while file search is the direct document-upload grounding capability. Reference topics: Foundry Agent Service memory, memory stores, File Search tool, vector stores, and grounded agent responses.
エージェントを含む Microsoft Foundry プロジェクトがあります。このエージェントは、Azure AI Search for Retrieval Augmented Generation (RAG) を使用しています。PDF 版の製品マニュアルを取り込んでインデックスを作成する予定です。意味的類似性マッチングをサポートするソリューションを構築する必要があります。このソリューションでは、ユーザーからの質問が製品マニュアルとは異なる表現を使用している場合でも、エージェントが関連データを取得できるようにする必要があります。
- A. 分析装置
- B. ベクトル検索
- C. 意味的ランキング
- D. サジェスター
Correct Answer: B 🗳️
Explanation: Only visible for VCEDumps members. You can sign-up / login (it's free).
注：このセクションには、同じシナリオと問題に関する複数の質問セットが含まれています。各質問には、問題に対する固有の解決策が提示されています。提示された解決策が、提示された目標を満たしているかどうかを判断する必要があります。セット内の複数の解決策が問題を解決できる場合もあります。また、セット内のどの解決策も問題を解決できない場合もあります。
このセクションの質問に回答すると、前のセクションに戻ることはできません。そのため、これらの質問は復習画面には表示されません。
エージェントを含む Microsoft Foundry プロジェクトがあります。このエージェントは、取得したポリシー文書から概要を生成します。
ユーザーからは、取得したコンテンツに規定の規制条項が含まれている場合でも、一部の回答では必要な規制条項が省略されているとの報告がある。
回答の完全性を改善する必要があります。
解決策：必要な句が欠落している場合にレスポンスを再生成するリフレクションパスを追加します。
これは目標を達成していると言えるでしょうか？
- A. はい
- B. いいえ
Correct Answer: A 🗳️
Explanation: Only visible for VCEDumps members. You can sign-up / login (it's free).
あなたは、カスタムAPIを使用して指定された場所の現在時刻を取得するエージェントを構築しています。カスタムAPIの機能をテストする必要があります。
このコマンドをどのように完了すればよいですか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
* Endpoint selection: assistants
* Function declaration property: tools=[
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The command is creating/configuring an assistant that has access to a custom function named get_current_time, so the REST resource shown in the exhibit is assistants .
For the function definition, current Azure OpenAI/Foundry function-calling syntax uses the tools collection.
A function tool is represented by an item with " type " : " function " and a nested " function " object containing its name, description, JSON parameter schema, and required arguments.
Microsoft explicitly documents that the older functions and function_call parameters have been deprecated and that tools and tool_choice should be used instead. Microsoft Learn Therefore, although the embedded exhibit ' s answer image shows functions=[, the currently verified Microsoft syntax is tools=[ .
content represents message content rather than an agent tool definition. tool_resources is used for resources required by certain hosted tools and is not where a custom function schema is declared.
The AI-103 Study Guide requires candidates to define tool schemas and integrate agent tools, including APIs and custom functions . Microsoft Learn
モデルを再学習させたり、知識を永続的に保存したりすることなく、正しい分類がどのようなものかモデルに示すことで、分類精度を向上させる必要があります。
- A. 少数ショット学習
- B. 思考の連鎖
- C. 検索拡張生成(RAG)
- D. ゼロショット学習
Correct Answer: A 🗳️
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