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Updated: Jul 30, 2026

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Microsoft AI-103日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI Apps and Agents on Azure
Exam Number:AI-103
Certificate Validity Period:1 year (renewable annually via free online assessment)
Exam Duration:100 minutes
Related Certifications:Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026)
Passing Score:700 / 1000
Real Exam Qty:50–60
Exam Price:$165 USD (varies by country/region)
Exam Format:Multiple choice, Scenario-based, Case study, Performance-based items
Available Languages:English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil)
Recommended Training:Microsoft Learn: Develop AI Apps and Agents on Azure
Azure AI Foundry Documentation
Exam Registration:Pearson VUE Registration
Microsoft Certification Dashboard
Sample Questions:Microsoft AI-103日本語 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103

Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Build conversational language understanding
  • 2. Use Azure AI Language services
  • 3. Perform sentiment analysis, entity recognition, and summarization
Topic 2: Plan and manage Azure AI solutions25–30%- 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
- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Configure model and agent deployments
  • 3. Monitor and maintain AI workloads
Topic 3: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Design knowledge mining pipelines
  • 2. Implement Azure AI Search
  • 3. Create and manage vector indexes
- Extract structured data from documents
  • 1. Process forms, invoices, and unstructured content
  • 2. Use Azure AI Document Intelligence
Topic 4: 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. Extract text and structure from images
  • 2. Use Azure AI Vision services
  • 3. Implement object detection and image classification
Topic 5: Implement generative AI and agentic solutions30–35%- 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
- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Select agent architecture patterns
  • 3. Manage state, memory, and context
  • 4. Implement multi-agent workflows and orchestration

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

1. 副操縦士は、会話の以前のやり取りに依存する複数の質問に答える必要があります。システムが複雑な質問をそれぞれ焦点を絞ったサブクエリに分割し、それらを並列実行し、回答を作成する前に結果を意味的に再ランク付けするようにしたい場合、Azure AI Search のどの機能がこれを提供しますか?

A) エージェントによる検索
B) 単一キーワードクエリにおけるトップk値の増加
C) 高い類似度閾値を持つ単一ベクトルクエリ
D) スコアリングプロファイルを使用したクラシックなキーワード検索


2. お客様は、Hub1 という名前の Azure AI Foundry ハブ、resource1 という名前の Azure OpenAI リソース、および User1 という名前のユーザーを含む Azure サブスクリプションを所有しています。
User1がHub1に新しいAzure AI Content Understandingプロジェクトを作成できるようにする必要があります。このソリューションは、最小権限の原則に従う必要があります。
User1にはどの役割を割り当てるべきですか?

A) Azure AI開発者
B) OpenAIの認知サービスユーザー
C) Azure AI 管理者
D) OpenAIの認知サービス貢献者


3. ホットスポットに関する質問
Azure AI Search を使用するエンリッチメント パイプラインを作成しています。ナレッジ ストアには、構造化されていない JSON データと、スキャンされた PDF ドキュメントのテキストが含まれています。
各データタイプには、どの投影法を使用すべきでしょうか?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。


4. ホットスポットに関する質問
内部Q&Aエージェントを含むMicrosoft Foundryプロジェクトがあります。
ユーザーがエージェントに質問した際に、以下のような問題が報告されています。
- 次の回答が増加しました:「関連情報なし」
見つかった"
- ピーク時間帯にHTTP 429レート制限超過エラーが定期的に発生
各問題の原因が、モデルの利用不可、リソース制限、または推論の失敗のいずれであるかを特定する必要があります。
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。


5. あなたは、一般公開されているウェブサイトからユーザーが生成した動画やテキストを処理する新しい販売システムを開発しています。
あなたは、ユーザーのデータが販売システムによって処理されたことをユーザーに通知する予定です。
これは、責任あるAIの原則のうち、どれを満たすのに役立ちますか?

A) 信頼性と安全性
B) 包括性
C) 透明性
D) 公平性


Solutions:

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

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