Try and practice the latest Microsoft : AI-103日本語 real questions & answers
Last Updated: Oct 05, 2026
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
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。
Correct Answer:
ホットスポットに関する質問
あなたは、検索拡張生成(RAG)ソリューションを含むMicrosoft Founcyプロジェクトを持っています。
クエリ、コンテキスト、レスポンス、正解データを含むラベル付きCSVデータセットを使用して、本番環境前の評価を実行する必要があります。評価では、以下の項目を測定する必要があります。
- 回答がユーザーの質問に対応しているかどうか
- 回答が提供された文脈によって裏付けられているかどうか
回答に機密情報または専有情報が含まれているかどうか
どのAI品質評価指標を使用すべきでしょうか？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。
Correct Answer:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
マルチモーダルエージェントは、ユーザーがアップロードした画像を処理します。攻撃者は、エージェントがそれを読み取って実行することを期待して、画像内に隠しテキスト指示を埋め込みます。Azure AI Content Safety のどの機能が、このような攻撃を検出するように設計されていますか？
- A. 暴力カテゴリのコンテンツフィルタが「高」に設定されています
- B. プロンプトシールドは、文書および間接的なプロンプト挿入を検出します。
- C. エージェントのファイル検索ツールを無効にする
- D. アナライザーの画像解像度しきい値を上げる
Correct Answer: B 🗳️
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あなたは生産ラインを備えた工場を所有しています。
生産ラインで作業する従業員の動きを検知・追跡するために、ビデオ映像を分析するソリューションを開発する必要があります。
ソリューションにはどのサービスを含めるべきでしょうか？
- A. Foundry Toolsの画像解析におけるAzure Vision
- B. Foundry ToolsのAzure Visionによる空間分析
- C. Azure AI Face
- D. Azure AI Video Indexer
Correct Answer: B 🗳️
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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. 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
- C. 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
- D. Foundry Tools の Azure Translator
Correct Answer: D 🗳️
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