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AI-103 試験練習問題

Microsoft Certified: Azure AI Apps and Agents Developer Associate

AI-103 の PDF 練習問題。資料の言語は 英語です。購入はこの試験のみで、自動更新はありません。

試験概要

AI-103 は「Microsoft Certified: Azure AI Apps and Agents Developer Associate」の認定試験です。試験形式、時間、対応言語は以下をご確認ください。最新の出題範囲や受験条件は公式サイトをご参照ください。

試験コード
AI-103
公式の試験・資格名
Microsoft Certified: Azure AI Apps and Agents Developer Associate
試験形式
Proctored assessment; interactive items may be included
試験時間
120 分
受験可能な言語
英語、簡体字中国語、繁体字中国語、フランス語、ドイツ語、日本語、韓国語、イタリア語、ポルトガル語(ブラジル)、スペイン語

公式試験の対応言語と練習問題ファイルの言語は異なります。試験の版と言語は公式の予約ページでご確認ください。

公式情報: 公式サイト
情報確認日:2026-10-20

対応言語

  • 英語
  • 簡体字中国語
  • 繁体字中国語
購入前にお試し

最初の 20 問を無料プレビュー

全 161 問

題庫の元の順序で表示します。回答欄を開くと、正解と収録されている解説を確認できます。

問題の言語:英語。問題文・選択肢・正解・解説は元の題庫の言語で表示します。

免責事項:ExamPico の問題はすべてインターネット上の情報を整理したもので、学習参考用として提供しています。

問題 16

Question#16Topic1

You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service.
The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1.
You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked.
What should you configure in the OpenAPI specification?

  1. A. a header parameter defined for each operation
  2. B. an Azure Key Vault connection
  3. C. an API key security scheme
  4. D. a Bearer token security scheme
正解と解説を表示

正解

C

解説

Azure AI Search (Azure Cognitive Search) indexes PDF content from Azure Blob Storage using the Blob indexer and makes it available for retrieval. For grounding an agent on internal product PDF sheets, Azure AI Search provides indexing, enrichment (OCR), and semantic/vector retrieval for RAG scenarios. See: https://learn.microsoft.com/azure/search/search-howto-indexing-azure-blob-storage and https://learn.microsoft.com/azure/search/overview

問題 17

Question#17Topic1

You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?

  1. A. Route all the requests to a smaller model.
  2. B. Use a model cascade that routes the requests to different models.
  3. C. Increase the value of the max_tokens parameter for all the requests.
  4. D. Route all the requests to the most capable model.
正解と解説を表示

正解

C

解説

Azure AI Search (Azure Cognitive Search) indexes PDF content from Azure Blob Storage using the Blob indexer and makes it available for retrieval. For grounding an agent on internal product PDF sheets, Azure AI Search provides indexing, enrichment (OCR), and semantic/vector retrieval for RAG scenarios. See: https://learn.microsoft.com/azure/search/search-howto-indexing-azure-blob-storage and https://learn.microsoft.com/azure/search/overview

問題 18

Question#18Topic1

HOTSPOT -
You have a Microsoft Foundry project that contains an internal Q&A agent.
Users report the following issues when they ask the agent questions:
An increase in the following response: “No relevant information found”
Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question illustration

正解を表示

正解

AC

問題 19

Question#19Topic1

You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase. You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?

  1. A. latency
  2. B. evaluation metrics
  3. C. run success rate
  4. D. token usage
正解と解説を表示

正解

C

解説

Azure AI Search (Azure Cognitive Search) indexes PDF content from Azure Blob Storage using the Blob indexer and makes it available for retrieval. For grounding an agent on internal product PDF sheets, Azure AI Search provides indexing, enrichment (OCR), and semantic/vector retrieval for RAG scenarios. See: https://learn.microsoft.com/azure/search/search-howto-indexing-azure-blob-storage and https://learn.microsoft.com/azure/search/overview

問題 20

Question#20Topic1

HOTSPOT -
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
Each workflow run must include a retrieval step before generating a response.
Tool calls must authenticate by using the published agent’s own identity.
Tool access must use an identity isolated from other project resources.
Tool access must use support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Question illustration

正解を表示

正解

CC

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