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Last Updated: Oct 09, 2026
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
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. キーワード検索のみ
- B. 意味検索のみ
- C. ハイブリッド検索
- D. ベクター検索のみ
Correct Answer: C 🗳️
Explanation: Only visible for Pass4SureQuiz members. You can sign-up / login (it's free).
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：画像を処理する前に、安全でないコンテンツをブロックするように画像モデレーションを設定します。
これは目標を達成していると言えるでしょうか？
- A. いいえ
- B. はい
Correct Answer: B 🗳️
Explanation: Only visible for Pass4SureQuiz members. You can sign-up / login (it's free).
エージェントを含む Microsoft Foundry プロジェクトがあります。このエージェントは、次のアクションを実行するために 2 つのツールを使用します。
- Azure AI Search を使用して、非公開製品から回答を取得します。
ドキュメント索引。
- ウェブ検索ツールを使用して、公開されている情報を取得します。
インターネット。
特定の実行において、エージェントがインターネットからのみ情報を確定的に取得するようにする必要があります。
tool_choiceはどのような値に設定すべきですか？
- A. {"type": "azure_ai-search"}
- B. {"type": "bing_grouding"}
- C. 「必須」
- D. 「車」
Correct Answer: B 🗳️
Explanation: Only visible for Pass4SureQuiz members. You can sign-up / login (it's free).
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 プロジェクトを作成します。
- B. Microsoft Foundry プロジェクトに Agent1 を追加します。
- C. Application Insights リソースをプロビジョニングします。
- D. Microsoft Foundry プレイグラウンドに Agent1 を追加します。
Correct Answer: A 🗳️
Explanation: Only visible for Pass4SureQuiz members. You can sign-up / login (it's free).
ホットスポットに関する質問
エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントは保存済みのアクセスキーを使用してAzureキーコンテナーからシークレットを取得しますが、これはキーレス認証情報の要件に違反します。
エージェントが機密情報を取得できることを保証する必要があります。解決策は最小権限の原則に従う必要があります。
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：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
Box 1: Enable a system-assigned managed identity at the project level
The correct configuration is to enable a system-assigned managed identity at the project level.
This is the correct approach. Azure AI Foundry (formerly Azure Machine Learning) allows you to enable a system-assigned managed identity directly at the project level. This identity acts as a keyless credential native to Azure. To follow the principle of least privilege, you then assign this project-level identity a specific role (such as Key Vault Secrets User) restricted only to the required Azure Key Vault. This ensures that the agent can retrieve secrets securely without exposing credentials to other projects or resources Box 2: Assign the Key Vault Secrets User role to the managed identity To comply with the keyless-credentials requirement and follow the principle of least privilege, you should configure the Key Vault authorization method to assign the Key Vault Secrets User role to the managed identity using Azure Role-Based Access Control (Azure RBAC).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/ai-gateway
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
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