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Updated: Sep 13, 2026
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Microsoft AI-103日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Exam Duration: | 100 minutes |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026) |
| Passing Score: | 700 / 1000 |
| Real Exam Qty: | 50–60 |
| Exam Price: | $165 USD (varies by country/region) |
| Exam Format: | Scenario-based, Multiple choice, Performance-based items, Case study |
| Available Languages: | Korean, Portuguese (Brazil), Spanish, French, German, Japanese, English, Chinese (Simplified) |
| Recommended Training: | Microsoft Learn: Develop AI Apps and Agents on Azure Azure AI Foundry Documentation |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Dashboard |
| Sample Questions: | Microsoft AI-103日本語 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103 |
Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 4: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
副操縦士は、会話の以前のやり取りに依存する複数の質問に答える必要があります。システムが複雑な質問をそれぞれ焦点を絞ったサブクエリに分割し、それらを並列実行し、回答を作成する前に結果を意味的に再ランク付けするようにしたい場合、Azure AI Search のどの機能がこれを提供しますか？
- A. エージェントによる検索
- B. 単一キーワードクエリにおけるトップk値の増加
- C. 高い類似度閾値を持つ単一ベクトルクエリ
- D. スコアリングプロファイルを使用したクラシックなキーワード検索
Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).
お客様は、Hub1 という名前の Azure AI Foundry ハブ、resource1 という名前の Azure OpenAI リソース、および User1 という名前のユーザーを含む Azure サブスクリプションを所有しています。
User1がHub1に新しいAzure AI Content Understandingプロジェクトを作成できるようにする必要があります。このソリューションは、最小権限の原則に従う必要があります。
User1にはどの役割を割り当てるべきですか？
- A. Azure AI開発者
- B. OpenAIの認知サービスユーザー
- C. Azure AI 管理者
- D. OpenAIの認知サービス貢献者
Explanation: Only visible for Pass4training members. You can sign-up / login (it's free).
ホットスポットに関する質問
Azure AI Search を使用するエンリッチメント パイプラインを作成しています。ナレッジ ストアには、構造化されていない JSON データと、スキャンされた PDF ドキュメントのテキストが含まれています。
各データタイプには、どの投影法を使用すべきでしょうか？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。
Explanation:
Box 1: Object projection
For the unstructured JSON data, you must use object projection, and for the extracted text data from the scanned PDF documents, you must use file projection.
Object Projection: Projects your data as a full JSON representation. It is ideal for maintaining the original structure alongside any applied AI enrichments within a single JSON document inside Azure Blob Storage.
Box 2: File projection
File Projection: Captures the binary or image extraction layer required during Optical Character Recognition (OCR) processing. It isolates and outputs raw text and graphic details directly from unstructured physical binary files into a designated container Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-concept-image-scenarios
ホットスポットに関する質問
内部Q&Aエージェントを含むMicrosoft Foundryプロジェクトがあります。
ユーザーがエージェントに質問した際に、以下のような問題が報告されています。
- 次の回答が増加しました：「関連情報なし」
見つかった"
- ピーク時間帯にHTTP 429レート制限超過エラーが定期的に発生
各問題の原因が、モデルの利用不可、リソース制限、または推論の失敗のいずれであるかを特定する必要があります。
どうすればよいですか？回答するには、回答欄で適切な選択肢を選んでください。
注：正解ごとに1ポイントが加算されます。
あなたは、一般公開されているウェブサイトからユーザーが生成した動画やテキストを処理する新しい販売システムを開発しています。
あなたは、ユーザーのデータが販売システムによって処理されたことをユーザーに通知する予定です。
これは、責任あるAIの原則のうち、どれを満たすのに役立ちますか？
- A. 信頼性と安全性
- B. 包括性
- C. 透明性
- D. 公平性
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