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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 3: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 4: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 5: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
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
あなたは、PolicyWriterとRskReviewerという名前の2つのエージェントを含むMicrosoft Foundryプロジェクトを持っています。
PolicyWriterは顧客のポリシーに関する不適切な更新を生成し、RiskReviewerはその草稿をレビューする。
ビジュアルビルダーでは、以下の要件を満たすワークフローを作成する必要があります。
- 手動介入なしで低リスクのアップデートを完了します
- エージェント全体で予測可能な実行を保証します
- 高度なアップデートにはユーザーの承認が必要です
何を設定すればよいですか?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:彗星の選択はそれぞれ1ポイントです。
Correct Answer:

Explanation:
Box 1: The human-in-the-loop template that pauses execution of the workflow for input Human-in-the-loop template is correct. This pattern allows the system to run automatically for predictable, node-by-node agent execution while explicitly pausing the workflow when a high-risk condition is met to wait for human intervention.
Box 2: Add a Condition statement
Condition statement is correct. You must evaluate the risk level (low vs. high) before deciding whether to finalize the policy. A condition node checks the risk score output from RiskReviewer. If it is low, it routes to auto-finalization. If it is high, it routes to an approval step.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/building-human-in-the-loop-ai-workflows-with-microsoft-agent-framework/4460342
あなたは、Microsoft Foundry で、検索拡張生成 (RAG) チャット アプリの評価を構成する予定です。
現実性、関連性、有害コンテンツのカテゴリごとにスコアを提供する必要があります。
どの2つの評価カテゴリーを使用できますか?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. リスクと安全性の指標
- B. AI品質(自然言語処理)指標
- C. AI品質(AI支援)指標
- D. 流暢性評価ツール
- E. 類似性評価器
Correct Answer: A,C 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
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. 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。
- B. エージェントのファイル検索ツールを無効にする
- C. アナライザーの画像解像度しきい値を上げる
- D. 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
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
スキャンされたPDF形式の請求書を処理するアプリケーションがあります。請求書のレイアウトは様々で、複数ページにわたる表が含まれています。
光学文字認識(OCR)を使用して合計金額と請求書番号を抽出するパイプラインがあります。しかし、文書構造が無視されるため、結果が不正確になることがよくあります。
OCR、レイアウト分析、およびテンプレート一般化フィールド抽出機能を提供するソリューションを実装する必要があります。このソリューションは、カスタムモデルのトレーニングを必要としないものでなければなりません。また、管理作業を最小限に抑える必要があります。
解決策には何を含めるべきでしょうか?
- A. Foundry ToolsにおけるAzureコンテンツの理解
- B. Azure Machine Learning モデル
- C. Foundry ToolsにおけるAzure言語
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
ホットスポットに関する質問
App1という名前のPythonアプリケーションがあり、それがProject1という名前のMicrosoft Foundryプロジェクトと統合されています。
App1が以下の要件を満たしていることを確認する必要があります。
- Microsoft Entra マネージド ID を使用して認証します
- Azure OpenAI Responses API を使用して、デプロイ済みのモデルにプロンプトを送信します。Python コードをどのように完成させるべきですか?回答するには、回答領域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:



