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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
Implement information extraction and knowledge mining10–15%- Extract structured data from documents
  • 1. Process forms, invoices, and unstructured content
  • 2. Use Azure AI Document Intelligence
- Build knowledge bases and search solutions
  • 1. Implement Azure AI Search
  • 2. Design knowledge mining pipelines
  • 3. Create and manage vector indexes
Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Perform sentiment analysis, entity recognition, and summarization
  • 2. Use Azure AI Language services
  • 3. Build conversational language understanding
- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
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. Plan for security, compliance, and responsible AI
  • 3. Design for scalability, availability, and cost optimization
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Implement multi-agent workflows and orchestration
  • 3. Integrate agents with external systems and data sources
  • 4. Manage state, memory, and context
- Build generative AI applications
  • 1. Implement function calling and tool use
  • 2. Implement prompt engineering and optimization
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Integrate Azure OpenAI and other models

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

Question 1

あなたはセマンティックカーネルを使用してアプリを開発しています。
アプリのプロンプトテンプレートには、複雑なオブジェクトを含める必要があります。ソリューションは、サブプロパティを含むオブジェクトをサポートする必要があります。
使用できるプロンプトテンプレートはどれですか?正解はそれぞれ完全な解答を示しています。
注:正解ごとに1ポイントが加算されます。

A. YAML
B. 液体
C. ハンドルバー
D. JSONL
E. セマンティックカーネル


Question 2

ホットスポットに関する質問
あなたはAzureサブスクリプションをお持ちです。
ユーザーのプロンプトに応じて架空の店舗を生成する新しいリソースを作成する必要があります。
このソリューションは、リソースが顧客管理キーを使用してデータを保護することを保証する必要があります。
スクリプトをどのように完成させればよいでしょうか?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。


Question 3

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. LUISランタイムAPI
D. アシスタントAPI


Question 4

あなたはMicrosoft Foundryプロジェクトを持っています。
社内ポリシー文書の検索ソリューションをサポートするには、モデルカタログからモデルをデプロイする必要があります。このモデルは、文書内のテキストとユーザーのクエリのベクトル表現を生成する必要があります。
どのタイプのモデルを使用すべきですか?

A. 大規模言語モデル(LLM)
B. 画像生成モデル
C. 埋め込みモデル
D. 小型言語モデル(SLM)


Question 5

ホットスポットに関する質問
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。
- ユーザーが直接アップロードすることで、文脈的な根拠を提供できるようにする
チャット中にドキュメントを閲覧する。
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。


Solutions:

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

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