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Last Updated: Oct 11, 2026
No. of Questions: 159 Questions & Answers with Testing Engine
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Demanding exams and busy schedules collide for most IT professionals, and the AI-103日本語 exam is no exception. PracticeMaterial resolves that collision with 159 verified practice questions for the Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) exam that respect the time you actually have.
Microsoft AI-103日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Available Languages: | English |
| Certificate Validity Period: | 1 year |
| Passing Score: | 700 |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Exam Format: | Case study, Scenario-based, Multiple choice, Hands-on labs |
| Related Certifications: | Microsoft Certified: Azure AI Apps and Agents Developer Associate |
| Real Exam Qty: | 40-60 |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or test center |
| Pre Condition: | Experience with Python programming and familiarity with Azure AI services, generative AI, and Microsoft Foundry is recommended. |
| 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: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 2: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 3: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze visual content
|
AI-103日本語 Material FAQ
The Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) exam requires a score of 700 to pass, and the official registration fee is $165 USD. Because the fee is significant, preparing thoroughly the first time is the practical choice.
Every session in the test engine is scored and broken down, so you see clearly which topics you handle well and which ones keep costing you points. That picture tells you where your remaining study time belongs — a far better guide than rereading everything evenly.
The Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) exam contains 40-60 questions, and you will have 100 minutes minutes to complete them. Practicing within the same limits builds the pacing the real exam demands.
Experience with Python programming and familiarity with Azure AI services, generative AI, and Microsoft Foundry is recommended.
You can confirm the current eligibility requirements on the official AI-103日本語 exam page before you register.
This package includes 159 practice questions for the Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) exam. Every answer is verified by an experienced team of experts, so you can trust what you study.
Yes. Once downloaded, the online test engine keeps running without an internet connection, on Windows, Mac, Android, or iOS. That means practice continues on flights, in waiting rooms, and anywhere else your day takes you — your AI-103日本語 preparation never has to pause for a missing signal.
The Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) practice questions are organized around the official exam objectives. Key topics include:
- Implement generative AI solutions (25-30%)
- Implement text analysis and information extraction solutions (10-15%)
- Implement agentic solutions (20-25%)
Comparing your practice scores against these topics shows you where the next session should go.
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
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。
Correct Answer:
Explanation:
Box 1: OpenAI
To generate fictional stores in response to user prompts, you need a generative AI model (such as GPT-3.5 or GPT-4). The Azure OpenAI service natively supports this generative capability and allows you to secure your deployed models and data using customer-managed keys (CMK).
Box 2: --assign-identity
When defining the Azure Key Vault resource script to enable customer-managed keys (CMK), you must use --assign-identity (or assign-identity).
Reference:
https://learn.microsoft.com/en-us/azure/search/search-how-to-managed-identities
ホットスポットに関する質問
あなたはMicrosoft Foundryプロジェクトを持っています。
- 次の条件を満たすカスタマーサポートエージェントを作成する必要があります
要件：
- 厳選された社内ポリシー文書に保存されている回答のみを根拠とする
リポジトリ
- 複数のチャットセッション間で顧客の設定を保持します
エージェントはどのように設定すればよいですか？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。
Correct Answer:
Explanation:
Box 1: Configure retrieval from approved data sources
You should configure retrieval from approved data sources to meet this requirement.
In Microsoft Foundry (and broader Azure AI Foundry / Azure OpenAI architectures), grounding an agent exclusively in company policy documents requires implementing a Retrieval-Augmented Generation (RAG) pattern. This ensures the AI model only answers using the provided context and does not hallucinate or rely on its public training data.
Box 2: Enable agent memory that uses persistent storage
To retain user preferences across completely separate chat sessions or conversations, you must use persistent agent memory. Traditional chat history only tracks messages within a single active thread or session. By contrast, the Memory Store feature provides a managed, long-term memory system that structurally indexes user preferences and facts across different devices and separate sessions.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-capabilities-for-azure-openai-on-your-data/4144636
あなたは、ユーザーがアップロードした複数のファイルを結合して処理するエージェントを構築する予定です。
エージェントの開発にAzure AI Agent Serviceを使用するかどうかを評価しています。
サービスにアップロードできる各ファイルの最大サイズはどれくらいですか？
- A. 256 MB
- B. 512 MB
- C. 1GB
- D. 10 MB
Correct Answer: B 🗳️
Explanation: Only visible for PracticeMaterial 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、GitHub リポジトリを使用しています。リポジトリには、エージェントの評価設定を定義する File1 という名前の YAM ファイルが含まれています。プルリクエスト (PR) が開かれたときに File1 で定義された評価を実行する GitHub Actions ワークフローを作成する必要があります。ワークフローはどのように構成すればよいでしょうか？
- A. project-endpoint をプロジェクトのエンドポイントに設定します。
- B. model-ployment-name をデプロイされたモデルに設定します。
- C. tenant-id を Microsoft Entra テナント ID に設定します
- D. evaluation-config を YAML ファイルのパスに設定します。
Correct Answer: A 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
開発者は、推論を実行してエージェントを使用するために、Microsoft Foundry プロジェクトのエンドポイントを呼び出すためのマネージド ID を必要とします。セキュリティ ポリシーでは、データ プレーンへのアクセスのみを許可する最小限の権限を持つロールが求められています。マネージド ID には、どの組み込みロールを割り当てるべきですか？
- A. Azure AI開発者
- B. 鋳造所ユーザー
- C. 認知サービス貢献者
- D. Foundryアカウント所有者
Correct Answer: B 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
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