Microsoft AI-103日本語 dumps - in .pdf
- Exam Code: AI-103日本語
- Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
- Updated: Sep 10, 2026
- Q & A: 159 Questions and Answers
- PDF Price: $69.99
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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 10, 2026
- Q & A: 159 Questions and Answers
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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 10, 2026
- Q & A: 159 Questions and Answers
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
| Topic 2: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 3: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
あなたはチャットボットを構築しています。
チャットボットがユーザー入力を個別のカテゴリに分類できることを確認する必要があります。カテゴリは動的であり、推論時に定義されなければなりません。
入力データの分類には、どのサービスを利用すべきですか？
- A. Azure AI言語カスタムテキスト分類
- B. Azure AI 言語のカスタム固有表現認識 (NER)
- C. Azure OpenAI テキスト分類
- D. Azure OpenAI テキスト要約
Explanation: Only visible for ValidTorrent 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。
エージェントは、データ取得ツールとしてAzure AI Searchを使用します。
エージェントがドキュメント内および埋め込み画像内のテキストに基づいて応答を生成できるように、PDFをAzure AI Searchインデックスに取り込む予定です。
利用者は、出典ファイルへのリンクを含む引用を求めている。
インデックス作成の際には、画像が内蔵の光学文字認識(OCR)機能の入力として使用できる構造に抽出されるようにする必要があります。
どのインデックス作成方法を採用すべきでしょうか？
- A. インデックスのコンテンツフィールドに対してOCRスキルを直接実行するスキルセット
- B. OCR入力を再構築するシェイパースキル
- C. 画像データを正規化された画像コレクションに抽出するインデクサー
- D. 画像データを検索可能なフィールドに書き込むための outputFieldMappings パラメータ
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財務チームは、請求書から請求書番号や合計金額などの名前付きフィールドを抽出する必要があり、信頼度の低い値を人間のレビュー担当者に送るために、フィールドごとの信頼度スコアも必要としています。どの Azure Content Understanding モードを使用すべきでしょうか？
- A. プロモード
- B. 信頼度スコアリングにはAzure AI Document Intelligenceが必要なため、どちらも該当しません。
- C. どちらのモードでも、信頼度スコアが返されるため、どちらでも構いません。
- D. 標準モード
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法務チームは、非構造化契約書から条項と推論フィールドを抽出する必要があります。必須フィールドの1つは契約終了日ですが、これは明示的に記載されていないため、開始日と契約期間から導き出す必要があります。チームにはラベル付きのトレーニングデータがありません。どのツールが最適でしょうか？
- A. ラベル付きサンプルでトレーニングされた Azure AI Document Intelligence カスタムモデル
- B. Foundry Tools の Azure Translator
- C. Azure AI Searchのセマンティックランカー
- D. 事前構築済みコントラクトまたはゼロショットカスタムアナライザーを使用したAzure Content Understanding
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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
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