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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Perform sentiment analysis, entity recognition, and summarization
  • 2. Build conversational language understanding
  • 3. Use Azure AI Language services
- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
Topic 2: Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Implement multi-agent workflows and orchestration
  • 3. Select agent architecture patterns
  • 4. Manage state, memory, and context
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Implement function calling and tool use
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Integrate Azure OpenAI and other models
Topic 3: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Design knowledge mining pipelines
  • 2. Create and manage vector indexes
  • 3. Implement Azure AI Search
- Extract structured data from documents
  • 1. Process forms, invoices, and unstructured content
  • 2. Use Azure AI Document Intelligence
Topic 4: Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Configure model and agent deployments
  • 3. Monitor and maintain AI workloads
- 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
Topic 5: Implement computer vision solutions10–15%- 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
- 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. 顧客サポートチャットアプリのエージェントを含むMicrosoft Foundryプロジェクトがあります。
エージェントはメモリストアとメモリ検索ツールを使用する。
会話履歴が複数のセッションにまたがって保持されないようにする必要があります。
メモリツールのスコープはどこに設定すべきですか?

A) {{$userId}}
B) グローバル
C) {{$conversationId}}
D) セッション


2. Microsoft Foundry プロジェクトがあり、Azure AI Search を使用してエージェントを内部ドキュメントに紐付けています。
最近のコンテンツ更新後、ユーザーからエージェントの回答の精度が低下したとの報告が寄せられている。
取得したコンテンツが、モデルが生成する応答に悪影響を与えているかどうかを特定する必要があります。
どの観測可能性シグナルを検証すべきでしょうか?

A) インデクサの状態と障害履歴
B) レイテンシー内訳トレース
C) グラウンディング評価指標
D) 予測ドリフト指標


3. 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) 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
B) 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)
C) Azure Kubernetes Service 上でのセルフホスト型デプロイメント
D) 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


4. ホットスポットに関する質問
あなたは、エージェントを含み、KV1という名前のAzureキーコンテナーを使用する、Project1という名前のMicrosoft Foundryプロジェクトを作成する予定です。
Project1からKV1への接続を設定する必要があります。
上腕二頭筋コードをどのように完成させるべきですか?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。


5. ホットスポットに関する質問
Microsoft Foundryプロジェクトがあり、その中にFoundry Agent Serviceを使用して構築されたカスタマーサポートエージェントが含まれています。
エージェントは、チケット発行ツールを通じてユーザーから提供されたスクリーンショットをAzureストレージにアップロードし、追加の分析に必要なBLOB URLを受け取ります。
エージェントの実行中は画像モデレーションを使用し、実行中に有害なコンテンツが返されないようにする必要があります。Azure AI Content Safety は、BLOB URL を使用して画像にアクセスする必要があります。
解決策は最小権限の原則に従わなければならない。
コンテンツセーフティの設定について、どのような項目を選択すべきでしょうか?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。


Solutions:

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

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