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- Exam Code: AI-103日本語
- Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
- Updated: Sep 13, 2026
- Q & A: 159 Questions and Answers
- PDF Version + PC Test Engine + Online Test Engine
- Value Pack Total: $139.98 $89.99
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Microsoft AI-103日本語 dumps - Testing Engine
- Exam Code: AI-103日本語
- Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
- Updated: Sep 13, 2026
- Q & A: 159 Questions and Answers
- Software Price: $69.99
- Testing Engine
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 2: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 4: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze visual content
|
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
言語モデルにプロンプトテキストを送信する前に、機密情報を削除するPythonアプリケーションがあります。そのアプリケーションには以下のコードが含まれています。
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。
Explanation:
Box 1: No
The audit list will not include entity records for "Contact" and "SSN" because neither word is a target PII entity type defined in the configuration.
The code limits scanning to Person and PhoneNumber via the piiCategories list.
"Contact" Definition: This is a regular English verb/noun, not a person's name or a telephone number.
"SSN" Definition: While "SSN" is a text label that points to sensitive data, it is a descriptor rather than an actual Social Security Number.
Box 2: Yes
For the given sample_text, text_for_model will include both or [email protected] and 859-
98-0987 completely unredacted.
The payload explicitly restricts the Personally Identifiable Information (PII) recognition to only two specific categories by setting piiCategories: ["Person", "PhoneNumber"].
Email Address Excluded: Because the Email category is omitted from the configuration list, the service bypasses the email address ([email protected]) and leaves it fully intact.
SSN Excluded: Similarly, because the USSSN (or equivalent Social Security Number) category is not specified in the piiCategories array, the SSN (859-98-0987) is completely ignored by the redaction policy and remains visible.
Box 3: Yes
text_for_model will contain entity type masks for "John Doe" and "312-555-1234", but only for those two specific items.
The piiCategories array explicitly requests Person and PhoneNumber.
Applied Redaction: The Azure AI Language service will replace "John Doe" and "312-555-1234" with masks like * or entity labels (depending on exact policy settings).
Skipped Data: The email ([email protected]) and the SSN (859-98-0987) will not be redacted because their categories (Email and USSSN) were omitted from your piiCategories list.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/concepts/conversations-entity-categories
ドラッグアンドドロップ問題
あなたは、工場生産ラインで製造された不良部品を検出するアプリケーションを開発しています。これらの部品は、あなたのビジネスに特有のものです。
一般的な障害を検出するには、Azure Custom Vision API を使用する必要があります。
どの3つの行動を順番に実行すべきでしょうか？回答するには、行動リストから適切な行動を回答欄に移動させ、正しい順序に並べ替えてください。
Explanation:
Step 1: Create a project
You must first set up a new project in the Custom Vision portal or via the SDK to store your data and configuration.
Step 2: Upload and tag images
Next, you need to upload photos of your specific factory components and draw bounding boxes around the faults to tag them.
Step 3: Train the object detection model
Because you need to identify and locate specific, localizable faults on a component, you must train an object detection model rather than a general classifier model.
Reference:
https://thegroundtruth.blog/tag/computervision/
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、表と埋め込まれたQRコードを含む、スキャンされたPDF形式の仕入先請求書を取り込みます。
エージェントは、抽出された出力においてPDFのレイアウトを保持し、後続の処理でセクションや表を参照できるようにする必要があります。
Foundry Tools で Azure Content Understanding を呼び出す予定です。
言語モデルの展開を必要とせずに、コンテンツとレイアウト要素を抽出し、QRコードを検出する必要があります。
どの内蔵アナライザーを使用すべきですか？
- A. 事前構築済みレイアウト
- B. プリビルド読み取り
- C. 事前構築済みドキュメントフィールドスキーマ
- D. 事前構築済みドキュメント検索
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Foundry Tools の Azure Constant Understanding を使用する App1 という名前の請求書処理アプリケーションがあります。
あなたは、以下の要件を満たす必要がある、Pipeline1という名前の新しいコンテンツ理解パイプラインを構築しています。
請求書とそれに関連する発注書を比較する
- 音声を静的なベンダー連絡先文書と照合して検証する
- 不一致箇所を含む単一の構造化出力を返す
Pipeline1を設定し、パイプラインを単一のアナライザーエンドポイントとして公開する必要があります。何を設定すればよいでしょうか？
- A. 請求書と発注書をアナライザーへの入力として使用する標準モードの複数ファイルタスク
- B. ベンダー契約ファイルを参考データとして使用するプロモードの複数ファイルタスク
- C. 抽出されたフィールドに対して信頼度スコアが有効になっている標準モードの単一ファイルタスク。
- D. 標準モードで提供されるベンダー契約書を分析中の追加文書として使用する単一ファイルタスク。
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あなたはAzureサブスクリプションをお持ちです。
あなたは、Azure OpenAI DALL-Eモデルを使用するアプリを開発する予定です。
モデルをデプロイする必要があります。
何を使うべきでしょうか？
- A. AzureポータルとMicrosoft Graph API
- B. Azure SDK for JavaScript および Azure Machine Learning Studio
- C. PythonおよびPowerShellコマンドレット用のAzure SDK
- D. Azure AI Studio および Azure コマンドラインインターフェイス (CLI)
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