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

AI-103日本語

Exam Code: AI-103-JPN

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

Updated: Oct 25, 2026

Q & A: 159 Questions and Answers

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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Monitor and maintain AI workloads
  • 2. Integrate with CI/CD pipelines
  • 3. Configure model and agent deployments
- Design Azure AI infrastructure
  • 1. Design for scalability, availability, and cost optimization
  • 2. Plan for security, compliance, and responsible AI
  • 3. Select appropriate Azure AI Foundry services
Implement computer vision solutions10–15%- Build multimodal solutions
  • 1. Process and analyze video content
  • 2. Combine vision and language capabilities
- 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 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 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. Design knowledge mining pipelines
  • 2. Implement Azure AI Search
  • 3. Create and manage vector indexes
Implement generative AI and agentic solutions30–35%- 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
- Design and implement intelligent agents
  • 1. Integrate agents with external systems and data sources
  • 2. Manage state, memory, and context
  • 3. Implement multi-agent workflows and orchestration
  • 4. Select agent architecture patterns

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

Question #1
Azure AI Studioでは、GPT-35 Turboモデルを使用してCompletionsプレイグラウンドを利用します。
プロンプトに以下のコードが含まれています。
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
コードの説明を作成するにはモデルが必要です。解決策はコストを最小限に抑え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. プロンプトに関数 F(説明) を追加します。
B. モデルを GPT-4-32k に変更します。
C. プロンプトに // 関数 F は何をしますか? を追加します。
D. 温度パラメータを1に設定します。


Question #2
Agent1という名前のカスタムエージェントが作成されました。
Microsoft Foundryを使用して、Agent1へのアクセスを制御し、アクティビティを監視する必要があります。
まず最初に何をすべきでしょうか?

A. Microsoft Foundry プロジェクトに Agent1 を追加します。
B. Application Insights リソースをプロビジョニングします。
C. Microsoft Foundry プロジェクトを作成します。
D. Microsoft Foundry プレイグラウンドに Agent1 を追加します。


Question #3
事前に構築された Azure 言語モデルを使用するアプリケーションがあります。
以下の要件を満たす要約を作成する必要があります。
- ビデオ内の各プレゼンターを特定し、各テキストファイルに属性を付与します。
文
- 元の文の順序を保持します
- 正確に3つの文を返します
どの言語サービス機能を使用すべきですか?

A. 抽象的要約
B. 抽出型要約
C. 感情分析
D. キーワード抽出


Question #4
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。


Question #5
ホットスポットに関する質問
感情分析と光学文字認識(OCR)を実行するために使用する新しいリソースを作成する必要があります。ソリューションは以下の要件を満たす必要があります。
単一のキーとエンドポイントを使用して、複数のサービスにアクセスできます。
今後利用する可能性のあるサービスの請求を一本化します。
- 将来的にFoundry ToolsでAzure Visionの使用をサポートする。
新しいリソースを作成するためのHTTPリクエストはどのように完了すればよいですか?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:正解ごとに1ポイントが加算されます。


Solutions:

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

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