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Microsoft AI-103日本語 Exam Syllabus Topics:

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

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

          1. Microsoft Foundry エージェントが、以下の内容を含む Azure Search インデックスからの応答を解析します。
          - 商品名と商品コードを検索できるテキストフィールド
          - 製品説明の埋め込みを格納するベクトル場
          ユーザーが以下の方法でインデックスを照会できるようにする必要があります。
          - 正確な製品名またはコード
          - 製品の自然言語による説明
          何を設定すればよいですか?

          A) ベクター検索のみ
          B) キーワード検索のみ
          C) 意味検索のみ
          D) ハイブリッド検索


          2. ドラッグアンドドロップ問題
          エージェントを含む Microsoft Foundry プロジェクトがあります。
          エージェントが複数の会話にわたってユーザー設定を記憶できるようにするには、長期記憶を有効にする必要があります。保存される記憶は、クライアントアプリケーションがユーザーIDを手動で生成することなく、認証済みユーザーごとに分離されている必要があります。
          Pythonコードを完成させるにはどうすればよいでしょうか?適切な値を正しい場所にドラッグして答えてください。各値は1回、複数回、または全く使用されない場合があります。
          注:正解ごとに1ポイントが加算されます。


          3. You are defining an agent in Microsoft Foundry Agent Service. The agent uses a catalogue model for reasoning, a system instruction that sets its goals, and a file search capability that reads a knowledge store. Which three components does this combination represent?

          A) 埋め込み、リトリーバー、ジェネレータ
          B) エンドポイント、APIキー、デプロイメント名
          C) トリガー、アクション、条件
          D) モデル、説明書、工具


          4. 注:このセクションには、同じシナリオと問題に関する複数の質問セットが含まれています。各質問には、問題に対する固有の解決策が提示されています。提示された解決策が、提示された目標を満たしているかどうかを判断する必要があります。セット内の複数の解決策が問題を解決できる場合もあります。また、セット内のどの解決策も問題を解決できない場合もあります。
          このセクションの質問に回答すると、前のセクションに戻ることはできません。そのため、これらの質問は復習画面には表示されません。
          画像のアップロードを受け付け、抽出した画像テキストを使用して応答を生成する、マルチモーダルなAI生成モデルをお持ちです。
          ユーザーが安全でない画像をアップロードしたり、モデルを操作するための隠された指示を画像に埋め込んだりできることが分かります。
          リスクを軽減するための対策を実施する必要があります。
          解決策:ドキュメントにプロンプ​​トシールドを設定します。
          これは目標を達成していると言えるでしょうか?

          A) はい
          B) いいえ


          5. ドラッグアンドドロップ問題
          サプライヤーから提出された調達文書を処理するMicrosoft Foundryプロジェクトがあります。
          Foundry ToolsのAzure Content Understandingを使用して、2つのパイプラインを実装する必要があります。
          解決策は以下の要件を満たす必要があります。
          - コスト効率の良いパイプラインをサポートする Pipeline1 という名前のパイプラインを含める。
          単体PDF形式の請求書を大量に処理する。
          - クロスドキュメントをサポートする Pipeline2 という名前のパイプラインを含める
          多段階推論と参照データを用いた検証。
          各パイプラインはどのように構成すればよいでしょうか? 回答するには、適切な構成を正しいパイプラインにドラッグしてください。各構成は、1回、複数回、またはまった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: D
          Question # 2
          Answer: Only visible for members
          Question # 3
          Answer: D
          Question # 4
          Answer: A
          Question # 5
          Answer: Only visible for members

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