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AI-103 Exam Practice Questions

Microsoft Certified: Azure AI Apps and Agents Developer Associate

AI-103 English PDF practice questions: 161 questions in source order, with options supported by the source format, answers and available explanations. Access covers this exam only. One-time payment; no automatic renewal.

Exam overview

For Azure AI developers building and operating AI applications and agents with Microsoft Foundry.

Exam code
AI-103
Official exam / credential
Microsoft Certified: Azure AI Apps and Agents Developer Associate
Exam format
Proctored assessment; interactive items may be included
Exam duration
120 minutes
Official exam languages
English, Simplified Chinese, Traditional Chinese, French, German, Japanese, Korean, Italian, Portuguese (Brazil), Spanish

Official exam languages and practice-question file languages are listed separately. Confirm the exam version and available languages when booking with the provider.

Official sources: Provider reference
Information checked: 2026-10-20

Supported languages

  • English
  • Simplified Chinese
  • Traditional Chinese
Try before you buy

First 20 practice questions

161 total questions

Questions follow the source order. Expand each answer to see its explanation.

Disclaimer: All questions on ExamPico are compiled from internet sources and are provided for study reference only.

Question 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
Show answer and explanation

Answer

C

Explanation

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

Question 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.
Show answer and explanation

Answer

C

Explanation

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

Question 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

Show answer

Answer

AC

Question 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
Show answer and explanation

Answer

C

Explanation

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

Question 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

Show answer

Answer

CC

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