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AI-103日本語
  • Exam Code: AI-103
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Sep 26, 2026
  • No. of Questions: 159 Questions and Answers
  • Download Limit: Unlimited
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Microsoft AI-103日本語 Exam Syllabus Topics:

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

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

エージェントを含む Microsoft Foundry プロジェクトがあり、GitHub リポジトリを使用しています。リポジトリには、エージェントの評価設定を定義する File1 という名前の YAM ファイルが含まれています。プルリクエスト (PR) が開かれたときに File1 で定義された評価を実行する GitHub Actions ワークフローを作成する必要があります。ワークフローはどのように構成すればよいでしょうか?

  • A. model-ployment-name をデプロイされたモデルに設定します。
  • B. tenant-id を Microsoft Entra テナント ID に設定します
  • C. evaluation-config を YAML ファイルのパスに設定します。
  • D. project-endpoint をプロジェクトのエンドポイントに設定します。
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Azure AIエージェントソリューションをお持ちです。
あなたは、ユーザー向けにデータを分析・要約し、データに基づいた推奨事項を生成するエージェントベースのアプリケーション「App1」を開発する予定です。App1は、技術的な知識を持たないビジネスユーザーが使用するアプリケーションであり、新たな予期せぬビジネス上の課題に適応し、時間の経過とともにパフォーマンスを向上させる必要があります。
App1で使用するエージェントの種類を特定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。
- ユーザーに基づいてエージェントのパフォーマンスを時間とともに適応および改善する
フィードバック。
ユーザーが情報に基づいた意思決定を行えるよう、個別の推奨事項を提供する。
決定。
アプリのパフォーマンスを最大限に高める。
どのタイプのエージェントを特定すべきでしょうか?

  • A. タスク自動化
  • B. 即時応答
  • C. 認知
  • D. 自律型
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).

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。
エージェントは保存済みのアクセスキーを使用してAzureキーコンテナーからシークレットを取得しますが、これはキーレス認証情報の要件に違反します。
エージェントが機密情報を取得できることを保証する必要があります。解決策は最小権限の原則に従う必要があります。
何を設定すればよいですか?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。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Box 1: Enable a system-assigned managed identity at the project level
The correct configuration is to enable a system-assigned managed identity at the project level.
This is the correct approach. Azure AI Foundry (formerly Azure Machine Learning) allows you to enable a system-assigned managed identity directly at the project level. This identity acts as a keyless credential native to Azure. To follow the principle of least privilege, you then assign this project-level identity a specific role (such as Key Vault Secrets User) restricted only to the required Azure Key Vault. This ensures that the agent can retrieve secrets securely without exposing credentials to other projects or resources Box 2: Assign the Key Vault Secrets User role to the managed identity To comply with the keyless-credentials requirement and follow the principle of least privilege, you should configure the Key Vault authorization method to assign the Key Vault Secrets User role to the managed identity using Azure Role-Based Access Control (Azure RBAC).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/ai-gateway
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references

トラフィック量の多いエージェントを含む Microsoft Foundry プロジェクトがあります。
最近のアップデート後、運用コストが大幅に増加しました。
監視の結果、エージェントへのユーザーアクセス量は変化していないことが確認されました。
リクエストまたはレスポンスの特性の変化が、増加の原因となっているのではないかと疑っている。
追加コストの原因が、モデルの入力サイズ、出力サイズ、またはツール使用範囲の拡大のいずれにあるかを特定する必要があります。
どの可観測性機能を使用すべきでしょうか?

  • A. レイテンシー
  • B. 実行成功率
  • C. 評価指標
  • D. トークンの使用
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).

2026年半ばに、Azure上で全く新しいエージェントアプリケーションの開発を開始し、Foundryエージェントの一般提供されサポートされているエントリーポイントを利用したいと考えています。どのAPIをターゲットにすべきでしょうか?

  • A. 従来のテキスト補完API
  • B. Foundry Agent Service を介したレスポンス API
  • C. アシスタントAPI
  • D. LUISランタイムAPI
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).

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