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

AI-103日本語 Exam Simulator
  • Exam Code: AI-103-JPN
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Aug 07, 2026
  • Q & A: 159 Questions and Answers
  • Microsoft AI-103日本語 Q&A - in .pdf

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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Manage state, memory, and context
  • 3. Integrate agents with external systems and data sources
  • 4. Implement multi-agent workflows and orchestration
- Build generative AI applications
  • 1. Integrate Azure OpenAI and other models
  • 2. Build retrieval-augmented generation (RAG) solutions
  • 3. Implement prompt engineering and optimization
  • 4. Implement function calling and tool use
Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Implement object detection and image classification
  • 2. Extract text and structure from images
  • 3. Use Azure AI Vision services
- Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Configure model and agent deployments
  • 2. Monitor and maintain AI workloads
  • 3. Integrate with CI/CD pipelines
- 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 text and speech analysis solutions10–15%- Implement speech capabilities
  • 1. Speech-to-text and text-to-speech integration
  • 2. Speech translation and speaker recognition
- Implement natural language processing
  • 1. Perform sentiment analysis, entity recognition, and summarization
  • 2. Use Azure AI Language services
  • 3. Build conversational language understanding
Implement information extraction and knowledge mining10–15%- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content
- 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:

1. App1 という名前のアプリケーションがあり、Foundry Tools の Azure Speech を使用してライブ通話を文字起こししています。
トランスクリプトには、英語とスペイン語の両方が含まれていることがよくあります。App1 は、各セグメントを Foundry Tools の Azure Translator に送信し、別の言語に翻訳します。
場合によっては、複数の言語が混在する部分では、翻訳が不完全または不正確になることがあります。
翻訳エラーを減らす必要があります。解決策は、文字起こし全体が確実に正しく翻訳されることを保証するものでなければなりません。
翻訳ツールにセグメントを送信する前に、何をすべきですか?

A) 文書翻訳を使用して、トランスクリプト全体を単一の文書として翻訳します。
B) 複数の言語が混在するセグメントを単一言語のセグメントに分割し、各セグメントを個別に翻訳します。
C) 翻訳依頼の自動言語検出を有効にします。
D) すべてのセグメントの翻訳依頼において、原文言語として英語を指定してください。


2. Azure AI Content Safety を構成して、生成アプリケーションでユーザーがアップロードした画像をモデレートするようにします。テスト中に、同僚が、ある画像が暴力の深刻度スコア 3 を返したと主張しました。
なぜこのような結果はあり得ないのか?

A) 画像コンテンツモデルは、深刻度値0、2、4、6のみを返します。
B) 重症度3は自傷行為のカテゴリーのみに適用されます
C) コンテンツセーフティは画像ではなくテキストのみを審査します。
D) 画像モデレーションは常にブロックまたは許可のバイナリフラグを返し、深刻度は返しません。


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


4. ホットスポットに関する質問
あなたは、検索拡張生成(RAG)ソリューションを含むMicrosoft Founcyプロジェクトを持っています。
クエリ、コンテキスト、レスポンス、正解データを含むラベル付きCSVデータセットを使用して、本番環境前の評価を実行する必要があります。評価では、以下の項目を測定する必要があります。
- 回答がユーザーの質問に対応しているかどうか
- 回答が提供された文脈によって裏付けられているかどうか
回答に機密情報または専有情報が含まれているかどうか
どのAI品質評価指標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。


5. Foundry Tools の Azure Constant Understanding を使用する App1 という名前の請求書処理アプリケーションがあります。
あなたは、以下の要件を満たす必要がある、Pipeline1という名前の新しいコンテンツ理解パイプラインを構築しています。
請求書とそれに関連する発注書を比較する
- 音声を静的なベンダー連絡先文書と照合して検証する
- 不一致箇所を含む単一の構造化出力を返す
Pipeline1を設定し、パイプラインを単一のアナライザーエンドポイントとして公開する必要があります。何を設定すればよいでしょうか?

A) 抽出されたフィールドに対して信頼度スコアが有効になっている標準モードの単一ファイルタスク。
B) ベンダー契約ファイルを参考データとして使用するプロモードの複数ファイルタスク
C) 請求書と発注書をアナライザーへの入力として使用する標準モードの複数ファイルタスク
D) 標準モードで提供されるベンダー契約書を分析中の追加文書として使用する単一ファイルタスク。


Solutions:

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

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