Microsoft AI-103日本語 dumps - 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: Sep 10, 2026
  • Q & A: 159 Questions and Answers
  • PDF Price: $69.99
  • Free Demo

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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: Sep 10, 2026
  • Q & A: 159 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $139.98  $89.99
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Microsoft AI-103日本語 dumps - Testing Engine

AI-103日本語 Testing Engine
  • Exam Code: AI-103日本語
  • Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
  • Updated: Sep 10, 2026
  • Q & A: 159 Questions and Answers
  • Software Price: $69.99
  • Testing Engine

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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
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. Design knowledge mining pipelines
  • 2. Create and manage vector indexes
  • 3. Implement Azure AI Search
Implement generative AI and agentic solutions30–35%- Build generative AI applications
  • 1. Integrate Azure OpenAI and other models
  • 2. Implement prompt engineering and optimization
  • 3. Implement function calling and tool use
  • 4. Build retrieval-augmented generation (RAG) solutions
- 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
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. Use Azure AI Language services
  • 2. Build conversational language understanding
  • 3. Perform sentiment analysis, entity recognition, and summarization
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Monitor and maintain AI workloads
  • 3. Configure model and agent deployments
- Design Azure AI infrastructure
  • 1. Select appropriate Azure AI Foundry services
  • 2. Plan for security, compliance, and responsible AI
  • 3. Design for scalability, availability, and cost optimization
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. Implement object detection and image classification
  • 2. Use Azure AI Vision services
  • 3. Extract text and structure from images

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

Question #1

Azure OpenAIリソースを含むAzureサブスクリプションをお持ちです。
与えられた商品説明から商品名を提案するアプリを開発する必要があります。
どのモデルを使用すべきですか?

  • A. GPT-4
  • B. ささやき
  • C. DALL-E
  • D. 埋め込み
Answer: A

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Question #2

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 における Azure コンテンツの理解
  • C. Foundry Tools の Azure Document Intelligence
  • D. Azure AI Video Indexer
Answer: C

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Question #3

大量の標準的な製品説明文を、迅速かつ一貫性を保ち、かつ低コストで20言語に翻訳するアプリケーションが必要です。テキストは簡潔で、業界特有のニュアンスは含まれていません。どの手法が最適でしょうか?

  • A. Azure AI ドキュメントインテリジェンス
  • B. Foundry Tools の Azure Translator
  • C. 20の対象言語それぞれに対して個別に微調整されたモデル
  • D. カスタムシステムプロンプトを使用した大規模言語モデル翻訳フロー
Answer: B

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Question #4

注:このセクションには、同じシナリオと問題に関する複数の質問セットが含まれています。各質問には、問題に対する固有の解決策が提示されています。提示された解決策が、提示された目標を満たしているかどうかを判断する必要があります。セット内の複数の解決策が問題を解決できる場合もあります。また、セット内のどの解決策も問題を解決できない場合もあります。
このセクションの質問に回答すると、前のセクションに戻ることはできません。そのため、これらの質問は復習画面には表示されません。
画像のアップロードを受け付け、抽出した画像テキストを使用して応答を生成する、マルチモーダルなAI生成モデルをお持ちです。
ユーザーが安全でない画像をアップロードしたり、モデルを操作するための隠された指示を画像に埋め込んだりできることが分かります。
リスクを軽減するための対策を実施する必要があります。
解決策:画像を処理する前に、安全でないコンテンツをブロックするように画像モデレーションを設定します。
これは目標を達成していると言えるでしょうか?

  • A. はい
  • B. いいえ
Answer: A

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Question #5

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

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