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

"Developing AI Apps and Agents on Azure (AI-103日本語版)", also known as AI-103 exam, is a Microsoft Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 159 Q&As to your AI-103 Exam preparation. In the AI-103 exam resources, you will cover every field and category in Azure AI Engineer Associate Certification helping to ready you for your successful Microsoft Certification.

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  • Updated on: Sep 13, 2026
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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Implement object detection and image classification
  • 3. Extract text and structure from images
- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
Implement text and speech analysis solutions10–15%- Implement speech capabilities
  • 1. Speech translation and speaker recognition
  • 2. Speech-to-text and text-to-speech integration
- 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%- Design Azure AI infrastructure
  • 1. Plan for security, compliance, and responsible AI
  • 2. Design for scalability, availability, and cost optimization
  • 3. Select appropriate Azure AI Foundry services
- Manage AI solution development lifecycle
  • 1. Integrate with CI/CD pipelines
  • 2. Monitor and maintain AI workloads
  • 3. Configure model and agent deployments
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
Implement generative AI and agentic solutions30–35%- Build generative AI applications
  • 1. Build retrieval-augmented generation (RAG) solutions
  • 2. Integrate Azure OpenAI and other models
  • 3. Implement function calling and tool use
  • 4. Implement prompt engineering and optimization
- Design and implement intelligent agents
  • 1. Implement multi-agent workflows and orchestration
  • 2. Integrate agents with external systems and data sources
  • 3. Manage state, memory, and context
  • 4. Select agent architecture patterns

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

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. 翻訳依頼の自動言語検出を有効にします。
  • 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. 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。
  • 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。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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ホットスポットに関する質問
あなたは、顧客からのコメントを収集し、公開フォーラムに投稿するPythonアプリケーションを持っています。
Azure AI Content Safetyにテキストコメントを送信し、応答から自傷行為の深刻度を返す必要があります。
コードをどのように完成させればよいでしょうか？回答するには、回答欄で適切な選択肢を選んでください。
注：正解ごとに1ポイントが加算されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Box 1: AnalyzeTextOptions(text=comment)
To set up the request in your Python script, you must use AnalyzeTextOptions(text=comment), passing the text as a single string rather than a list.
Example:
Build the request options focusing on Text analysis
# You can optionally restrict analysis to specific categories
request_options = AnalyzeTextOptions(
text=user_comment,
categories=[TextCategory.SELF_HARM]
Box 2: client.analyze_text(request)
You must use client.analyze_text(request)
1. Send the request: Call client.analyze_text(request) to retrieve the multi-severity results.
2. Extract the self-harm result: Locate the self-harm categories from the response list.
3. Return the severity: Access the .severity attribute
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/ai-contentsafety-readme

Project1という名前のMicrosoft Foundryプロジェクトがあります。
プロジェクト1には、PDF形式の仕入先請求書を処理するアプリケーションが含まれています。
Foundry ToolsでAzure Document Intelligenceを構成して、PDFのセクションと表構造を保持するMarkdown出力を生成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。
あなたはどうすべきでしょうか？

  • A. ドキュメントを分析する際に、content=markdown を設定します。
  • B. PDFを解析する際に、出力設定を「図」にしてください。
  • C. output_content_format=ContentFormat.MARKDOWN の値を設定します。
  • D. 信頼度閾値を上げる。
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

あなたはセマンティックカーネルを使用してアプリを開発しています。
アプリのプロンプトテンプレートには、複雑なオブジェクトを含める必要があります。ソリューションは、サブプロパティを含むオブジェクトをサポートする必要があります。
使用できるプロンプトテンプレートはどれですか？正解はそれぞれ完全な解答を示しています。
注：正解ごとに1ポイントが加算されます。

  • A. ハンドルバー
  • B. セマンティックカーネル
  • C. YAML
  • D. 液体
  • E. JSONL
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

ホットスポットに関する質問
貴社は、Microsoft Foundryプロジェクト「Project1」において、顧客サポートエージェントの試験運用を行っています。
Project1は既存のApplication Insightsリソースに接続されており、同社のサポートチームは「トレース」タブで実行結果を確認します。
Foundryエージェントサービスは、以下の動作を実行するように構成されています。
- Application Insights の接続文字列を取得するには、次の関数を呼び出します。
project_client.telemetry.get_application_insights_connection_string()。
- configure_azure_monitor(connection_string=...) を呼び出して有効化します
テレメトリー。
別のLangChainサービスがOpenTelemetryを使用するように構成されており、以下の設定になっています。
- AzureAIOpenTelemetryTracer(connection_string=...,
enable_content_recording=False)
- config={"callbacks":[azure_tracer]} を使用してトレーサーを渡します。
会社の方針には以下の要件があります。
- LangChainとOpenTelemetryからのテレメトリは区別可能である必要がある
同じ Application Insights リソース内。
- プロンプトやツールに秘密情報や認証情報を保存してはいけません
引数、またはスパン属性。
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」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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