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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| 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:
HTTPリクエストを正常に実行しました。
.../regenerateKey?...
with:
{ " keyName " : " Key2 " }
結果はどうなりますか？
- A. プライマリーサブスクリプションキーとセカンダリーサブスクリプションキーがローテーションされました。
- B. 新しいクエリキーが生成されました。
- C. 新しいセカンダリーサブスクリプションキーが作成されました。
- D. Azure Key Vault に Azure AI Services のキーが生成されました。
Correct Answer: C 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
Agent1がstorage1に保存されている製品情報を使用するための、計画されている変更と技術要件をサポートするソリューションを提案する必要があります。
推薦状には何を含めるべきでしょうか？
- A. Bing検索でグラウンディング
- B. Foundry Tools の Azure Translator
- C. Foundry Tools の Azure Document Intelligence
- D. Azure AI Search
Correct Answer: D 🗳️
Explanation: Only visible for PassCollection 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、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. 事前構築済みドキュメント検索
- B. 事前構築済みレイアウト
- C. 事前構築済みドキュメントフィールドスキーマ
- D. プリビルド読み取り
Correct Answer: B 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
App1 という名前のアプリケーションがあり、Foundry Tools の Azure Speech を使用してライブ通話を文字起こししています。
トランスクリプトには、英語とスペイン語の両方が含まれていることがよくあります。App1 は、各セグメントを Foundry Tools の Azure Translator に送信し、別の言語に翻訳します。
場合によっては、複数の言語が混在する部分では、翻訳が不完全または不正確になることがあります。
翻訳エラーを減らす必要があります。解決策は、文字起こし全体が確実に正しく翻訳されることを保証するものでなければなりません。
翻訳ツールにセグメントを送信する前に、何をすべきですか？
- A. 複数の言語が混在するセグメントを単一言語のセグメントに分割し、各セグメントを個別に翻訳します。
- B. 翻訳依頼では、すべてのセグメントについて原文言語として英語を指定してください。
- C. 翻訳依頼の自動言語検出を有効にする。
- D. 文書翻訳を使用して、トランスクリプト全体を単一の文書として翻訳します。
Correct Answer: A 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
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。
Correct Answer:
Explanation:
The PII analysis is explicitly restricted to Person and PhoneNumber categories. Therefore, only entities belonging to those requested classes are selected for the configured entity-mask redaction.
The word Contact is normal surrounding text and is not a Person or PhoneNumber entity. The SSN is a different PII category and is not included in the configured category list. Therefore, the first statement is No .
For the same reason, the email address and SSN remain in the resulting redactedText because neither category is requested. The second statement is therefore Yes .
John Doe is a Person entity and 312-555-1234 is a PhoneNumber entity. With the entityMask redaction policy, those selected spans are replaced by masks that identify the relevant entity types, so the third statement is Yes .
The code also builds an audit collection from the entities returned by the analysis operation, allowing the application to separately record what was detected while sending only the redacted string to the language model.
Study Guide alignment: configure detection of sensitive content and prepare safe textual inputs for downstream generative models . Microsoft Learn
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