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Download Microsoft : AI-103日本語 Questions & Answers as PDF & Test Software

Last Updated: Oct 16, 2026

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Microsoft AI-103日本語 Exam Syllabus Topics:

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

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。
スキャンされたテキスト、表、複数列レイアウトを含む、様々な形式のドキュメントを処理する必要があります。抽出されたコンテンツはドキュメントの構造を保持し、後続の処理のためにMarkdown形式に変換する必要があります。
最初に何を設定すべきですか?

  • A. マルチモーダルモデルを使用するAzure OpenAI Responses API呼び出し
  • B. Azure Language in Foundry Tools のテキスト分析モデルのデプロイ
  • C. Foundry Tools の Azure コンテンツ理解アナライザー
  • D. 生成型チャット完了リクエスト
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

Foundry Tools の Azure Speech リソースがあり、そこにカスタムの音声認識モデルがカスタムエンドポイントにデプロイされています。エージェントはこのエンドポイントを使用して、リアルタイムの音声認識を実行します。
お客様のカスタム音声認識モデルの有効期限が近づいています。
モデルの有効期限が切れた場合、どのような動作が想定されますか?

  • A. カスタムモデルは、モデルの有効期限が切れると自動的に削除されます。
  • B. 音声認識リクエストは、モデルが手動で削除されるまで、期限切れのカスタムモデルを引き続き使用します。
  • C. 音声認識要求は、同じロケールの最新の基本モデルにフォールバックします。
  • D. 新しいカスタムモデルがデプロイされるまで、音声認識リクエストは4xxエラーを返します。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #3

ドラッグアンドドロップ問題
デプロイ済みのチケットトリアージエージェントを含むMicrosoft Foundryプロジェクトがあります。
エージェントがツールを必要とする場合でも、ツールを呼び出さずに応答することがあるということに気づくでしょう。
エージェントが実行中にツールを呼び出すようにする必要があります。
Pythonコードを完成させるにはどうすればよいでしょうか?適切な値を正しいターゲットにドラッグして答えてください。各値は1回、複数回、または全く使用されない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
注:正解ごとに1ポイントが加算されます。

Reveal Solution  Discussion  0

Correct Answer:

Question #4

ユーザーが写真をアップロードできるようにするサポートエージェントをデプロイしています。
アップロードされた画像を自動的に分類し、有害コンテンツを検出する必要があります。ソリューションは、有害度レベルに基づいてコンテンツをブロックする必要があります。
あなたはどうすべきでしょうか?

  • A. 画像モデレーションを実施する。
  • B. Foundry Tools の Azure Vision を使用して、光学文字認識 (OCR) 出力にキーワード スキャンを適用します。
  • C. ブロックリストを使用します。
  • D. プロンプトシールドを有効にする。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #5

ホットスポットに関する質問
デプロイ済みのチャットモデルを含むMicrosoft Foundryプロジェクトがあります。
Pythonサービスがあり、そこからモデルにAPIリクエストが送信されます。このサービスは、生成された出力を承認済みの応答パターンと比較する自動検証システムと統合されています。
関係者によると、わずかな文言の違いが検証結果の不一致を引き起こしているとのことだ。
出力の安定性を向上させるには、リクエストパラメータを更新する必要があります。解決策は推論の質を最大限に高めるものでなければなりません。
Python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。

Reveal Solution  Discussion  0

Correct Answer:

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