Microsoft AI-103日本語 : Developing AI Apps and Agents on Azure (AI-103日本語版)

  • Exam Code: AI-103日本語
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Jul 26, 2026
  • Q & A: 107 Questions and Answers

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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Manage state, memory, and context
  • 3. Integrate agents with external systems and data sources
  • 4. Implement multi-agent workflows and orchestration
- Build generative AI applications
  • 1. Implement function calling and tool use
  • 2. Integrate Azure OpenAI and other models
  • 3. Implement prompt engineering and optimization
  • 4. Build retrieval-augmented generation (RAG) solutions
Implement text and speech analysis solutions10–15%- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
- Implement natural language processing
  • 1. Use Azure AI Language services
  • 2. Perform sentiment analysis, entity recognition, and summarization
  • 3. Build conversational language understanding
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Implement object detection and image classification
  • 3. Extract text and structure from images
Plan and manage Azure AI solutions25–30%- Design Azure AI infrastructure
  • 1. Plan for security, compliance, and responsible AI
  • 2. Design for scalability, availability, and cost optimization
  • 3. Select appropriate Azure AI Foundry services
- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Configure model and agent deployments
  • 3. Monitor and maintain AI workloads
Implement information extraction and knowledge mining10–15%- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content
- Build knowledge bases and search solutions
  • 1. Implement Azure AI Search
  • 2. Design knowledge mining pipelines
  • 3. Create and manage vector indexes

Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:

1. あなたは、PDF形式で保存されている人事(HR)ポリシーに関する質問に答えるソリューションを設計しています。
特定の質問に対して、毎回同じ回答が返されるようにする必要があります。また、開発の手間を最小限に抑えるソリューションでなければなりません。
ソリューションにはどのサービスを含めるべきでしょうか?

A) Azure AI 言語
B) Azure Machine Learning
C) Azure AI ドキュメントインテリジェンス
D) Azure OpenAI


2. ホットスポットに関する質問
エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントは、内部コンテンツを取得したり、外部APIを呼び出したりするためにツールを使用します。エージェントは、モデルがツールを呼び出すタイミングを決定するように構成されています。
コンプライアンスワークフロー用のエージェントを公開する必要があります。ソリューションは以下の要件を満たす必要があります。
- 各ワークフロー実行には、生成前に取得ステップを含める必要があります。
応答。
ツール呼び出しは、公開されたエージェント自身の認証情報を使用して認証する必要があります。
身元。
ツールへのアクセスには、他のプロジェクトとは隔離されたIDを使用する必要があります。
リソース。
ツールへのアクセスには、監査追跡機能のサポートが必要です。
どうすればよいですか?回答するには、回答欄で適切な選択肢を選んでください。
注:正解ごとに1ポイントが加算されます。


3. あなたは薬局向けの音声エージェントを開発しています。音声による質問を受け付け、合成音声で応答し、一般的な音声モデルでは聞き間違えやすい専門的な医薬品名を正確に認識する必要があります。どの機能を構成すべきでしょうか?

A) エージェント内の音声テキスト変換のためのカスタム音声モデル
B) 音声ストリームに対するAzure AI Document Intelligence
C) Azure Translatorを使用して音声を変換します
D) デフォルトのテキスト読み上げ音声のみ


4. 法務チームは、非構造化契約書から条項と推論フィールドを抽出する必要があります。必須フィールドの1つは契約終了日ですが、これは明示的に記載されていないため、開始日と契約期間から導き出す必要があります。チームにはラベル付きのトレーニングデータがありません。どのツールが最適でしょうか?

A) Azure AI Searchのセマンティックランカー
B) Foundry Tools の Azure Translator
C) ラベル付きサンプルでトレーニングされた Azure AI Document Intelligence カスタムモデル
D) 事前構築済みコントラクトまたはゼロショットカスタムアナライザーを使用したAzure Content Understanding


5. 製品に関する質問に回答するSemantic Kernelエージェントがあります。
あなたは、注文状況に関する質問に答えられるように、エージェントの機能を拡張することを計画しています。
エージェントが注文状況に関する質問にできるだけ迅速に回答できるようにする必要があります。また、開発の手間を最小限に抑えるソリューションが求められます。
解決策には何を含めるべきでしょうか?

A) エージェント
B) プラグイン
C) 関数
D) 種子


Solutions:

Question # 1
Answer: D
Question # 2
Answer: Only visible for members
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: B

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