Updated: Sep 05, 2026
No. of Questions: 159 Questions & Answers with Testing Engine
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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure AI |
| Exam Number: | AI-103 |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 1 year (renewable via Microsoft Learn assessment) |
| Available Languages: | English |
| Exam Price: | USD 165 (may vary by region) |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: Azure Developer Associate Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure AI Fundamentals |
| Exam Format: | Drag and drop, Case studies, Multiple response, Scenario-based questions, Multiple choice |
| Exam Duration: | 120-180 |
| Recommended Training: | Microsoft Learn - Azure AI Engineer Learning Path Azure OpenAI Service Documentation |
| Exam Registration: | Pearson VUE Microsoft Exams Microsoft Certification Dashboard |
| Sample Questions: | Microsoft AI-103日本語 Sample Questions |
| Exam Way: | Online proctored exam or authorized test center |
| Pre Condition: | Recommended: experience with Azure services, Python or C#, and basic machine learning concepts. Prior knowledge of Azure AI-102 or equivalent is helpful but not mandatory. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/ |
| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
Question 1
ホットスポットに関する質問
あなたはAzureサブスクリプションをお持ちです。
ユーザーのプロンプトに応じて架空の店舗を生成する新しいリソースを作成する必要があります。
このソリューションは、リソースが顧客管理キーを使用してデータを保護することを保証する必要があります。
スクリプトをどのように完成させればよいでしょうか?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。
Question 2
あなたは、大量のトラフィックを処理するチャットアプリを提供するMicrosoft Foundryプロジェクトを持っています。
ほとんどの問い合わせは簡単なよくある質問(FAQ)ですが、中には高度な論理的思考を必要とするものもあります。
複雑な質問に対する回答の質を低下させることなく、一般的な問い合わせに対するコストと遅延を削減する必要があります。
あなたはどうすべきでしょうか?
A. すべてのリクエストをより小さなモデルにルーティングします。
B. すべてのリクエストを最も能力の高いモデルにルーティングします。
C. すべてのリクエストに対して max_tokens パラメータの値を増やします。
D. リクエストを異なるモデルにルーティングするモデルカスケードを使用します。
Question 3
Foundry Tools の Azure Speech リソースがあり、そこにカスタムの音声認識モデルがカスタムエンドポイントにデプロイされています。エージェントはこのエンドポイントを使用して、リアルタイムの音声認識を実行します。
お客様のカスタム音声認識モデルの有効期限が近づいています。
モデルの有効期限が切れた場合、どのような動作が想定されますか?
A. 新しいカスタムモデルがデプロイされるまで、音声認識リクエストは4xxエラーを返します。
B. 音声認識リクエストは、モデルが手動で削除されるまで、期限切れのカスタムモデルを引き続き使用します。
C. カスタムモデルは、モデルの有効期限が切れると自動的に削除されます。
D. 音声認識要求は、同じロケールの最新の基本モデルにフォールバックします。
Question 4
顧客サポートエージェントを含む Microsoft Foundry プロジェクトがあるとします。このエージェントは、応答を生成する前に、内部のナレッジ API ツールを呼び出します。
ユーザーから以下の問題が報告されています。
- 一部のリクエストは完了までに15秒以上かかる場合があります。
- 知識APIが期待されるデータを返した場合でも、一部の応答が誤っていることがあります。
大規模言語モデル(LLM)呼び出し、ツール呼び出し、およびタイミング情報の順序を確認するには、個々のエージェントの実行を検査する必要があります。
どの可観測性機能を使用すべきでしょうか?
A. トレース
B. モニタリング
C. 安全指標
D. トークンの使用
Question 5
ホットスポットに関する質問
あなたは、エージェントを含み、KV1という名前のAzureキーコンテナーを使用する、Project1という名前のMicrosoft Foundryプロジェクトを作成する予定です。
Project1からKV1への接続を設定する必要があります。
上腕二頭筋コードをどのように完成させるべきですか?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。
Solutions:
| Question 1 Answer: Only visible for members | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: Only visible for members |
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