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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement generative AI and agentic solutions30–35%- Build generative AI applications
  • 1. Integrate Azure OpenAI and other models
  • 2. Build retrieval-augmented generation (RAG) solutions
  • 3. Implement prompt engineering and optimization
  • 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
Topic 2: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Create and manage vector indexes
  • 2. Design knowledge mining pipelines
  • 3. Implement Azure AI Search
- Extract structured data from documents
  • 1. Process forms, invoices, and unstructured content
  • 2. Use Azure AI Document Intelligence
Topic 3: 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. Build conversational language understanding
  • 3. Perform sentiment analysis, entity recognition, and summarization
Topic 4: Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Monitor and maintain AI workloads
  • 3. Configure model and agent deployments
- Design Azure AI infrastructure
  • 1. Select appropriate Azure AI Foundry services
  • 2. Design for scalability, availability, and cost optimization
  • 3. Plan for security, compliance, and responsible AI
Topic 5: Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Implement object detection and image classification
  • 2. Extract text and structure from images
  • 3. Use Azure AI Vision services
- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities

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

1. 財務チームは、請求書から請求書番号や合計金額などの名前付きフィールドを抽出する必要があり、信頼度の低い値を人間のレビュー担当者に送るために、フィールドごとの信頼度スコアも必要としています。どの Azure Content Understanding モードを使用すべきでしょうか?

A) 標準モード
B) プロモード
C) どちらのモードでも、信頼度スコアが返されるため、どちらでも構いません。
D) 信頼度スコアリングにはAzure AI Document Intelligenceが必要なため、どちらも該当しません。


2. あるチームは、Foundryカタログからモデルを可能な限り迅速にセットアップし、消費したトークン分だけを支払い、仮想マシン(VM)の割り当て量を一切設定せずにデプロイしたいと考えています。どのデプロイオプションが最適でしょうか?

A) マネージドコンピューティング(マネージドエンドポイント)の展開
B) Azure Kubernetes Service 上でのセルフホスト型デプロイメント
C) 開発者ワークステーション上で実行されるローカルコンテナ
D) サーバーレスAPIデプロイメント、別名モデル・アズ・ア・サービス(MaaS)


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


4. PDF形式のポリシーマニュアルを取り込むAzure AI Searchインデクサーをお持ちです。
クライアントアプリケーションは、テキストと画像の両方について境界ポリゴンを持つページレベルの引用を表示する必要があります。
Azure AI Search スキルセットに、組み込みのマルチモーダルコンテンツ抽出スキルを 1 つ追加する必要があります。ソリューションは、次の要件を満たす必要があります。
テキストと画像の位置メタデータを提供する。
複数ページにまたがる表を抽出する。
何を追加すればいいですか?

A) GenAIプロンプト
B) ドキュメントレイアウト
C) 文書抽出
D) Azureコンテンツ理解


5. ユーザーが写真をアップロードできるようにするサポートエージェントをデプロイしています。
アップロードされた画像を自動的に分類し、有害コンテンツを検出する必要があります。ソリューションは、有害度レベルに基づいてコンテンツをブロックする必要があります。
あなたはどうすべきでしょうか?

A) プロンプトシールドを有効にする。
B) 画像モデレーションを実施する。
C) ブロックリストを使用します。
D) Foundry Tools の Azure Vision を使用して、光学文字認識 (OCR) 出力にキーワード スキャンを適用します。


Solutions:

Question # 1
Answer: A
Question # 2
Answer: D
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: B

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