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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?

A. Enable a managed identity for each application and call Azure AI Search directly.
B. Call Azure AI Search directly from each application by using Microsoft Entra authentication.
C. Configure an Azure AI Search connection in Project1 and reference the connection in each application.
D. Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.

Question 2

Hotspot Question
You have a Python application that redacts sensitive information before sending prompt text to a language model. The application has the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Question 3

You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?

A. embeddings
B. Whisper
C. GPT-4
D. DALL-E

Question 4

You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support a search solution for internal policy documents. The model must generate vector representations of the text in the documents and of user queries.
Which type of model should you use?

A. an image generation model
B. a large language model (LLM)
C. an embedding model
D. a small language model (SLM)

Question 5

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?

A. Route all the requests to the most capable model.
B. Increase the value of the max_tokens parameter for all the requests.
C. Route all the requests to a smaller model.
D. Use a model cascade that routes the requests to different models.

Solutions: