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

  • Exam Code: AI-103日本語
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Sep 08, 2026     Q & A: 159 Questions and Answers

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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Integrate with CI/CD pipelines
  • 3. Configure model and agent deployments
- Design Azure AI infrastructure
  • 1. Plan for security, compliance, and responsible AI
  • 2. Select appropriate Azure AI Foundry services
  • 3. Design for scalability, availability, and cost optimization
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- 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
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Manage state, memory, and context
  • 2. Integrate agents with external systems and data sources
  • 3. Implement multi-agent workflows and orchestration
  • 4. Select agent architecture patterns
- Build generative AI applications
  • 1. Build retrieval-augmented generation (RAG) solutions
  • 2. Implement prompt engineering and optimization
  • 3. Integrate Azure OpenAI and other models
  • 4. Implement function calling and tool use
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
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. Create and manage vector indexes
  • 2. Design knowledge mining pipelines
  • 3. Implement Azure AI Search

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

Question 1

Azure Storage アカウントに、大量の画像ファイルと PDF ドキュメントが保存されています。これらのドキュメントには表形式のデータが含まれています。
テーブルをデータベースにインポートできる構造化された形式に抽出する必要があります。開発作業を最小限に抑えるソリューションが必要です。どのような方法を用いるべきでしょうか?

A. Foundry Tools における Azure コンテンツの理解
B. Azure AI Video Indexer
C. Foundry Tools の Azure Vision
D. Foundry Tools の Azure Document Intelligence


Question 2

エージェントとイメージ生成モデルのデプロイメントを含む Microsoft Foundry プロジェクトがあります。
このエージェントは、ユーザーが提供した商品写真からオリジナルの画像を生成します。
生成された画像が、提供された写真の製品アイデンティティと視覚的特徴を維持し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. 温度パラメータの値を減らします。
B. リクエストにプロンプ​​トと入力画像を含めます。
C. 接地検出フィルターを適用します。
D. input_fidelity パラメータを high に設定します。


Question 3

モデルのデプロイメントを含む Microsoft Foundry プロジェクトがあります。
Azure OpenAI v1 APIとDefaultAzureCredentialを使用してデプロイメントを呼び出すアプリケーションがあります。
御社の開発者は、az login を実行した後でも、推論リクエストを送信する際に HTTP 403 エラーを受け取ります。
開発者がモデル推論を実行できることを確認する必要があります。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。
開発者にはどのロールベースアクセス制御(RBAC)ロールを割り当てるべきですか?

A. 寄稿者
B. コグニティブサービスユーザー
C. OpenAIの認知サービスユーザー
D. コグニティブサービスデータリーダー


Question 4

あなたは、ユーザーがアップロードした複数のファイルを結合して処理するエージェントを構築する予定です。
エージェントの開発にAzure AI Agent Serviceを使用するかどうかを評価しています。
このサービスにアップロードできるファイルの最大サイズはどれくらいですか?

A. 1GB
B. 1TB
C. 10GB
D. 100 GB


Question 5

マルチモーダルエージェントは、ユーザーがアップロードした画像を処理します。攻撃者は、エージェントがそれを読み取って実行することを期待して、画像内に隠しテキスト指示を埋め込みます。Azure AI Content Safety のどの機能が、このような攻撃を検出するように設計されていますか?

A. エージェントのファイル検索ツールを無効にする
B. プロンプトシールドは、文書および間接的なプロンプト挿入を検出します。
C. 暴力カテゴリのコンテンツフィルタが「高」に設定されています
D. アナライザーの画像解像度しきい値を上げる


Solutions:

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

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