AI-103 Practice Questions

You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.

The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional

reasoning.

You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure Al

Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.

What should you configure for Content Safety? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You are deploying a support agent that enables users to upload photos.

You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.

What should you do?

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?

You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.

You fine-tune a baseline speech to text model for the en-us locale and publish the model.

The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.

You need to set the project property to the correct I

D.  

To what should you set the project property?

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?

You have a Microsoft Foundry project that contains an agent.

You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.

How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.

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?

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?

You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.

You need to implement two pipelines by using Azure Content Understanding in Foundry Tools. The solution must meet the following requirements:

• Include a pipeline named Pipeline1 that supports cost-effective, high-volume processing of standalone PDF invoices.

• Include a pipeline named Pipeline2 that supports cross-document validation by using multi-step reasoning and reference data.

How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

You have a Microsoft Foundry project that contains an agent.

The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.

You use Azure Content Understanding in Foundry Tools to process the PDFs.

You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.

Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.

You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding locations within the source document.

What should you do?