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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You have a Microsoft Foundry project named Project1 that contains the following:
- An OpenAPI tool that calls an external API
- A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?

A. Enable identity passthrough so that the tool uses the Microsoft Entra token of the caller.
B. Configure the tool to use the default connection of Project1.
C. Connect the tool to Connection1.
D. Add the API key header manually to the OpenAPI specification.

Question 2

Hotspot Question
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
- Retain user preferences across multiple conversations.
- Enable users to provide contextual grounding by directly uploading
documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.
The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.
You need to reduce response variability, without modifying the prompt or reducing factual accuracy.
What should you do for the model?

A. Increase the max_tokens parameter.
B. Increase the temperature parameter.
C. Decrease the temperature parameter.
D. Remove stop sequences from the requests.

Question 4

You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?

A. Retrieval Augmented Generation (RAG)
B. zero-shot learning
C. chain of thought
D. few-shot learning

Question 5

You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?

A. a generative chat completion request
B. an Azure Content Understanding in Foundry Tools analyzer
C. an Azure OpenAI Responses API call that uses a multimodal model
D. an Azure Language in Foundry Tools text analysis model deployment

Solutions: