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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You are developing a new sales system that will process user-generated video and text from a public-facing website.
You plan to notify users that their data has been processed by the sales system.
Which responsible AI principle does this help meet?

A) inclusiveness
B) transparency
C) fairness
D) reliability and safety

2. Hotspot Question
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. You have an Azure subscription.
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
What should you use?

A) the Azure SDK for JavaScript and Azure Machine Learning Studio.
B) the Azure portal and Microsoft Graph API
C) Microsoft Foundry and the Azure Command Line Interface (CLI)
D) the Azure SDK for Python and PowerShell cmdlets.

4. You have a Microsoft Foundry project that contains an agent and uses a GitHub repository. The repository contains a YAM file named File1 that defines the evaluation settings of the agent. You need to create a GitHub Actions workflow that runs the evaluation defined in File1 when a pull request (PR) is opened. How should you configure the workflow?

A) Set evaluation-config to the path of the YAML file.
B) Set tenant-id to the Microsoft Entra tenant ID
C) Set project-endpoint to the endpoint of the project.
D) Set model-deployment-name to the deployed model.

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

Solutions: