Microsoft AI-103日本語 Q&A - in .pdf

  • Exam Code: AI-103日本語
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Aug 29, 2026
  • Q & A: 159 Questions and Answers
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  • Exam Code: AI-103日本語
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Aug 29, 2026
  • Q & A: 159 Questions and Answers
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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Implement generative AI solutions25-30%- Develop generative AI applications
  • 1. Build retrieval-augmented generation solutions
  • 2. Use Azure OpenAI and Foundry models
  • 3. Implement prompt engineering
- Optimize and evaluate models
  • 1. Evaluate responses and grounding
  • 2. Implement multimodal AI capabilities
  • 3. Configure content filters and safety
Implement text analysis and information extraction solutions10-15%- Analyze and extract information
  • 1. Extract entities and structured data
  • 2. Implement natural language processing
  • 3. Use document intelligence services
Plan and manage Azure AI solutions25-30%- Plan Azure AI resources
  • 1. Manage deployments and monitoring
  • 2. Select Azure AI services and Foundry resources
  • 3. Configure authentication and security
- Manage AI solution lifecycle
  • 1. Implement CI/CD for AI applications
  • 2. Monitor model and application performance
  • 3. Apply responsible AI practices
Implement computer vision solutions10-15%- Analyze visual content
  • 1. Implement OCR and visual understanding
  • 2. Use multimodal vision APIs
  • 3. Process images and video
Implement agentic solutions20-25%- Build AI agents
  • 1. Create autonomous and multi-agent workflows
  • 2. Integrate tools and external knowledge
  • 3. Configure memory and orchestration
- Manage agent operations
  • 1. Monitor and debug agents
  • 2. Secure agent interactions
  • 3. Implement scalable deployments

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

Question 1

エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントの知識源は、Azure Blob Storageに保存されているスキャン済みのPDFトラブルシューティングガイドのセットです。ガイドページは2列レイアウトと表で構成されています。
PDFを処理するには、Foundry ToolsのAzure Content Understandingを使用します。
処理済みのコンテンツをインデックスに取り込み、検索拡張生成(RAG)に利用し、抽出したフィールドを後続の自動化のために保存する予定です。
関係者は、抽出された各フィールド値が元のPDFのどこから来たのかを確認できなければならず、信頼性の低い抽出結果は手動レビューのために回送する必要がある。
コンテンツ理解ドキュメントアナライザーの出力には、フィールドごとの信頼度スコアと、ソースドキュメント内の位置を示すソース情報が含まれていることを確認する必要があります。
あなたはどうすべきでしょうか?

A. enableSegmentをtrueに設定します。
B. アナライザーを、すべてのフィールドに対して生成抽出を使用するように設定します。
C. estimateFieldSourceAndConfidence を有効にします。
D. ラベル付きサンプルを提供する。


Question 2

開発者は、推論を実行してエージェントを使用するために、Microsoft Foundry プロジェクトのエンドポイントを呼び出すためのマネージド ID を必要とします。セキュリティ ポリシーでは、データ プレーンへのアクセスのみを許可する最小限の権限を持つロールが求められています。マネージド ID には、どの組み込みロールを割り当てるべきですか?

A. 鋳造所ユーザー
B. Azure AI開発者
C. Foundryアカウント所有者
D. 認知サービス貢献者


Question 3

Microsoft Foundry プロジェクトにチャット アプリがあり、Azure AI Search のベクトル化インデックスがあります。
以下の要件を満たすには、インデックスに接続する必要があります。
複雑な質問では、複数の情報チャンクから情報を取得する必要があります。
複数ターンにわたる会話は、情報検索計画に影響を与える必要がある。
遅延を削減するため、データ取得は並列で実行する必要があります。
どの検索方法を用いるべきでしょうか?

A. 反復検索
B. 思考の連鎖
C. 古典的な検索拡張生成(RAG)
D. エージェント型検索拡張生成(RAG)


Question 4

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、Azure AI Search の Retrieval Augmented Generation (RAG) を使用します。
あなたはPDF形式の製品マニュアルを取り込んでインデックス化する計画を立てています。
意味的類似性マッチングをサポートするソリューションを構築する必要があります。このソリューションは、ユーザーからの質問が製品マニュアルとは異なる表現を使用している場合でも、エージェントが関連データを取得できるようにする必要があります。
どのインデックス作成方法を採用すべきでしょうか?

A. 分析装置
B. ベクトル検索
C. 意味的ランキング
D. サジェスター


Question 5

Project1という名前のMicrosoft Foundryプロジェクトがあります。
プロジェクト1には、PDF形式の仕入先請求書を処理するアプリケーションが含まれています。
Foundry ToolsでAzure Document Intelligenceを構成して、PDFのセクションと表構造を保持するMarkdown出力を生成する必要があります。このソリューションは、開発作業を最小限に抑える必要があります。
あなたはどうすべきでしょうか?

A. 信頼度閾値を上げる。
B. PDFを解析する際に、出力設定を「図」にしてください。
C. ドキュメントを分析する際に、content=markdown を設定します。
D. output_content_format=ContentFormat.MARKDOWN の値を設定します。


Solutions:

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

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