Microsoft AI-103日本語 Q&A - in .pdf

  • AI-103日本語 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 21, 2026
  • Q & A: 159 Questions and Answers
  • Convenient, easy to study.
    Printable Microsoft AI-103-JPN PDF Format. It is an electronic file format regardless of the operating system platform.
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  • Q & A: 159 Questions and Answers
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  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
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  • Q & A: 159 Questions and Answers
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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
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
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
Plan and manage Azure AI solutions25–30%- 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
- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Monitor and maintain AI workloads
  • 3. Configure model and agent deployments
Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Build conversational language understanding
  • 2. Use Azure AI Language services
  • 3. Perform sentiment analysis, entity recognition, and summarization
- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
Implement generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Manage state, memory, and context
  • 3. Select agent architecture patterns
  • 4. Implement multi-agent workflows and orchestration
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Implement function calling and tool use
  • 3. Integrate Azure OpenAI and other models
  • 4. Build retrieval-augmented generation (RAG) solutions

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

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


2. ホットスポットに関する質問
あなたは、PolicyWriterとRskReviewerという名前の2つのエージェントを含むMicrosoft Foundryプロジェクトを持っています。
PolicyWriterは顧客のポリシーに関する不適切な更新を生成し、RiskReviewerはその草稿をレビューする。
ビジュアルビルダーでは、以下の要件を満たすワークフローを作成する必要があります。
- 手動介入なしで低リスクのアップデートを完了します
- エージェント全体で予測可能な実行を保証します
- 高度なアップデートにはユーザーの承認が必要です
何を設定すればよいですか?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:彗星の選択はそれぞれ1ポイントです。


3. あなたは、大量のトラフィックを処理するチャットアプリを提供するMicrosoft Foundryプロジェクトを持っています。
ほとんどの問い合わせは簡単なよくある質問(FAQ)ですが、中には高度な論理的思考を必要とするものもあります。
複雑な質問に対する回答の質を低下させることなく、一般的な問い合わせに対するコストと遅延を削減する必要があります。
あなたはどうすべきでしょうか?

A) すべてのリクエストに対して max_tokens パラメータの値を増やします。
B) すべてのリクエストを最も能力の高いモデルにルーティングします。
C) すべてのリクエストをより小さなモデルにルーティングします。
D) リクエストを異なるモデルにルーティングするモデルカスケードを使用します。


4. 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) 重症度3は自傷行為のカテゴリーのみに適用されます
B) 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。
C) 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。
D) 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。


5. Microsoft Foundry Agent Service を使用して構築されたカスタマーサポートエージェントがあります。このエージェントは、Azure OpenAI モデルのデプロイメントを呼び出します。
負荷テスト中に、呼び出しが断続的に失敗し、HTTP 429 レート制限超過エラーが返されます。
負荷がかかった状態での通話失敗を減らし、信頼性を向上させるためには、スロットリングを適切に処理する必要があります。このソリューションは、サービスおよびモデルの制限内に収まるものでなければなりません。
あなたはどうすべきでしょうか?

A) アップロードされたコンテンツをより小さなファイルに分割します。
B) 新しいスレッドを作成し、すぐに呼び出しを再試行します。
C) 指数バックオフとジッターを使用する再試行ポリシーを実装します。
D) 登録ツールの数を減らす。


Solutions:

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

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