Study with AI-103日本語 Actual Test Questions

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Updated: Sep 02, 2026

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Microsoft AI-103日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI Apps and Agents on Azure AI
Exam Number:AI-103
Exam Price:USD 165 (may vary by region)
Available Languages:English
Exam Format:Multiple response, Scenario-based questions, Drag and drop, Multiple choice, Case studies
Passing Score:700/1000
Related Certifications:Microsoft Certified: Azure Developer Associate
Microsoft Certified: Azure AI Fundamentals
Microsoft Certified: Azure AI Engineer Associate
Exam Duration:120-180
Real Exam Qty:40-60
Certificate Validity Period:1 year (renewable via Microsoft Learn assessment)
Recommended Training:Microsoft Learn - Azure AI Engineer Learning Path
Azure OpenAI Service Documentation
Exam Registration:Microsoft Certification Dashboard
Pearson VUE Microsoft Exams
Sample Questions:Microsoft AI-103日本語 Sample Questions
Exam Way:Online proctored exam or authorized test center
Pre Condition:Recommended: experience with Azure services, Python or C#, and basic machine learning concepts. Prior knowledge of Azure AI-102 or equivalent is helpful but not mandatory.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/

Microsoft AI-103日本語 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement Natural Language Processing Solutions- Translation and multilingual support
- Text analytics and summarization
- Language understanding and intent recognition
Topic 2: Knowledge Mining and Information Retrieval- RAG (Retrieval Augmented Generation) patterns
- Azure AI Search configuration
- Indexing and semantic search
Topic 3: Develop Generative AI Applications and Agents- AI agents architecture
  • 1. Agent orchestration and workflows
    • 2. Memory and state management
      - Azure OpenAI Service integration
      • 1. Function calling and tool use
        • 2. Prompt engineering and prompt flow design
          Topic 4: Implement Computer Vision Solutions- OCR and document intelligence
          - Image classification and object detection
          Topic 5: Plan and Manage Azure AI Solutions- Azure AI resource provisioning and configuration
          - Model selection and lifecycle management
          - Responsible AI principles and governance

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

          Question 1

          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. セマンティックカーネル
          B. JSONL
          C. 液体
          D. YAML
          E. ハンドルバー


          Question 2

          ホットスポットに関する質問
          Azure AI Search を使用するエンリッチメント パイプラインを作成しています。ナレッジ ストアには、構造化されていない JSON データと、スキャンされた PDF ドキュメントのテキストが含まれています。
          各データタイプには、どの投影法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。


          Question 3

          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 Blob Storageに保存されているドキュメントから構築された知識ソースを使用します。これらのドキュメントには、複数ページの表を含むデジタルスキャンされたPDFファイルが含まれます。
          プレーンテキストのみを抽出する取り込みジョブを使用しているため、テーブル構造、見出し、ページ番号などのメタデータが失われています。
          ユーザーからは、複数のページにわたる特定の表の行を取得する必要があるという質問が頻繁に寄せられます。
          スキャンされたPDFに対して光学文字認識(OCR)を実行し、表と見出しを構造を考慮したチャンクとして保持し、各チャンクにページ番号メタデータを保存する、検索拡張生成(RAG)パイプライン用の取り込みジョブを設定する必要があります。
          データ取り込みジョブはどのように設定すればよいですか?

          A. OCRとページレベルのチャンキングを使用します。
          B. 高度なデータ解析を使用してドキュメントを再取り込みします。
          C. 基本的な構文解析と固定サイズのチャンキングを使用します。
          D. ページレベルのOCR抽出を使用し、各ページを単一のチャンクとして保存します。


          Question 4

          あなたは、Azure AI を使用してユーザーが性的に露骨な画像を共有することを防止する画像共有アプリを開発しています。
          不適切な画像が正しく識別されるようにする必要があります。また、開発の手間を最小限に抑えるソリューションでなければなりません。
          何を使うべきでしょうか?

          A. Azure AI Content Safety Studio
          B. Azure AI Studio
          C. Azure AI Vision の Vision Studio
          D. ビジュアルスタジオ


          Question 5

          ドラッグアンドドロップ問題
          あなたは、Foundry Tools の Azure Content Understanding を使用してマーケティング動画を分析する Microsoft Foundry プロジェクトを持っています。
          動画のセグメンテーションが有効になっています。
          各ビデオセグメントの配色を記述するJSONフィールドを出力するようにアナライザーを設定する必要があります。
          アナライザーの設定方法を教えてください。適切な値を正しいターゲットにドラッグして設定します。各値は、1回、複数回、または全く使用しない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
          注:正解ごとに1ポイントが加算されます。


          Solutions:

          Question 1
          Answer: C,E
          Question 2
          Answer: Only visible for members
          Question 3
          Answer: A
          Question 4
          Answer: A
          Question 5
          Answer: Only visible for members

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