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 03, 2026
  • Q & A: 159 Questions and Answers
  • PDF Price: $69.99

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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 03, 2026
  • Q & A: 159 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
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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 03, 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 generative AI and agentic solutions30–35%- Design and implement intelligent agents
  • 1. Implement multi-agent workflows and orchestration
  • 2. Manage state, memory, and context
  • 3. Select agent architecture patterns
  • 4. Integrate agents with external systems and data sources
- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Integrate Azure OpenAI and other models
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Implement function calling and tool use
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Configure model and agent deployments
  • 3. Integrate with CI/CD pipelines
- Design Azure AI infrastructure
  • 1. Select appropriate Azure AI Foundry services
  • 2. Design for scalability, availability, and cost optimization
  • 3. Plan for security, compliance, and responsible AI
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. Perform sentiment analysis, entity recognition, and summarization
  • 3. Build conversational language understanding
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
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. Process and analyze video content
  • 2. Combine vision and language capabilities

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。
エージェントは、データ取得ツールとしてAzure AI Searchを使用します。
エージェントがドキュメント内および埋め込み画像内のテキストに基づいて応答を生成できるように、PDFをAzure AI Searchインデックスに取り込む予定です。
利用者は、出典ファイルへのリンクを含む引用を求めている。
インデックス作成の際には、画像が内蔵の光学文字認識(OCR)機能の入力として使用できる構造に抽出され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. 画像データを検索可能なフィールドに書き込むための outputFieldMappings パラメータ
B. OCR入力を再構築するシェイパースキル
C. インデックスのコンテンツフィールドに対してOCRスキルを直接実行するスキルセット
D. 画像データを正規化された画像コレクションに抽出するインデクサー


Question 2

財務チームは、請求書から請求書番号や合計金額などの名前付きフィールドを抽出する必要があり、信頼度の低い値を人間のレビュー担当者に送るために、フィールドごとの信頼度スコアも必要としています。どの Azure Content Understanding モードを使用すべきでしょうか?

A. どちらのモードでも、信頼度スコアが返されるため、どちらでも構いません。
B. プロモード
C. 信頼度スコアリングにはAzure AI Document Intelligenceが必要なため、どちらも該当しません。
D. 標準モード


Question 3

あなたは、一般公開されているウェブサイトからユーザーが生成した動画やテキストを処理する新しい販売システムを開発しています。
あなたは、ユーザーのデータが販売システムによって処理されたことをユーザーに通知する予定です。
これは、責任あるAIの原則のうち、どれを満たすのに役立ちますか?

A. 信頼性と安全性
B. 公平性
C. 包括性
D. 透明性


Question 4

あなたは、自然な音声対話をサポートするために、Microsoft Foundry プロジェクトでエージェント ワークフローを作成しています。
エージェントは、連続的な音声入力を受け取り、推論のためにその入力をテキストに変換し、その後、音声による応答をユーザーに返す必要があります。ワークフローは以下の要件を満たす必要があります。
- ターン交代のダイナミクスをサポートし、エージェントが
ユーザーが話し終える前に音声が出力される。
会話体験を維持するために、低遅延で動作します。
リアルタイムのエージェントとのやり取りでは、音声認識と音声合成の両方を有効にする必要があります。
あなたはどうすべきでしょうか?

A. 埋め込みモデルを使用して音声をエンコードし、その後、音声をテキストと音声にデコードします。
B. バッチ文字起こしを使用して音声入力を変換し、エージェントからテキスト応答を返します。
C. 受信音声にはリアルタイム音声認識を使用し、エージェントの応答にはテキスト読み上げを使用します。
D. 音声翻訳を使用して音声を別の言語に変換し、翻訳されたテキストを返します。


Question 5

Project1という名前のMicrosoft Azure AI Foundryプロジェクトがあります。
あなたは、生成型AIモデルを使用してProject1に接続し、チャットを行うApp1という名前のアプリを作成する予定です。
Azure AI Foundry SDKを使用して、App1をProject1に接続する必要があります。このソリューションは、開発作業を最小限に抑える必要があります。
App1では何を設定すればよいですか?

A. SASCredentialsオブジェクト
B. 接続糸
C. AIProjectClientオブジェクト
D. プロジェクトスコープキー


Solutions:

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

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