Free Microsoft AI-103 Exam Dumps Questions & Answers

Hotspot Question
You plan to create a Microsoft Foundry project named Project1 that will contain an agent and use an Azure key vault named KV1.
You need to configure a connection from Project1 to KV1.
How should you complete the Bicep code? To answer, select the appropriate options in the answer area?
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: AzureKeyVault
To configure a connection from a Microsoft Foundry project to an Azure Key Vault, you must use the AzureKeyVault connection category.
Box 2: AccountManagedIdentity
To connect a Microsoft Foundry project to an Azure Key Vault, you must use AccountManagedIdentity as the authType.
Microsoft Foundry secures its outbound connections to core Azure storage and security infrastructure using Microsoft Entra ID role-based access control (RBAC). Using AccountManagedIdentity completely eliminates the need to handle or rotate explicit credentials or api keys within your configuration.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/set-up-key-vault-connection

Hotspot Question
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.

Correct Answer:

You have a web app named App1 that processes user prompts by integrating with a Microsoft Foundry project named Project1. App1 performs the following actions:
- Sends prompts directly to a model by using the Azure OpenAI Responses API
- Invokes the Azure AI Content Safety tool by using a Foundry
connection within the same request
You need to configure end-to-end visibility into each step of the request workflow.
What should you do?

• A.Enable logging by using Foundry Local.
• B.Route requests through the Azure OpenAI endpoint.
• C.Enable application tracing in Project1.
• D.Enable logging by using the client SDK for Content Safety.

Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware

Drag and Drop Question
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.

Correct Answer:

Explanation:
Box 1: "{{$userId}}"
User Isolation (Scope)
Setting scope="{{$userId}}" instructs the Microsoft Foundry Agent Service to automatically map and partition memory to the logged-in individual using their Microsoft Entra user identity template.
The platform handles this dynamically from request headers, removing the need for manual client-side user ID management Box 2: [memory_tool] Tool List: The tools configuration parameter expects an iterable containing instantiated tool objects. Wrapping memory_tool inside a Python list ([memory_tool]) exposes the capability to the agent definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/memory-usage?pivots=python