AI-901

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The dumps for AI-901 exam was last updated on May 08,2026 .

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Question#1

You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users.
What should you configure?

A. tokens per minute (TPM)

B. system instructions

C. temperature

D. max completion tokens

Explanation:
To create an AI agent that follows a specific role and behavior, you configure system instructions. Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior.
Option A. tokens per minute (TPM) controls throughput quota, not behavior.
Option C. temperature controls response randomness/creativity, not the agent’s role.
Option D. max completion tokens controls response length, not the agent’s role or behavioral rules.
Therefore, the correct answer is B. system instructions.

Question#2

DRAG DROP
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
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.

Explanation:
client = OpenAI(
api_key="...",
base_url="https://resource1.openai.azure.com/openai/v1/",
)
response = client.responses.create(
model="my-mini-gpt",
...
)
For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:
https://<resource-name>.openai.azure.com/openai/v1/
In the question, the Azure OpenAI resource is named Resource1, so the first blank must be resource1. Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.azure.com/openai/v1/ path.
For the model parameter, Azure OpenAI requires the deployment name, not the underlying model name. Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.
The deployed model is gpt-4.1-mini, but the deployment name is my-mini-gpt. Therefore, the second blank must be:
model="my-mini-gpt"
So the correct selections are:
base_url blank = resource1
model blank = my-mini-gpt

Question#3

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

Explanation:
Statement 1: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.
Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No
Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement.
Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system’s output. = Yes
Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.

Question#4

HOTSPOT
Select the answer that correctly completes the sentence.

Explanation:
When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data.
Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.
The other options are incorrect:
a CSV file attachment is not an image input format for vision analysis.
an MP4 video stream is video content, not an image format.
a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.

Question#5

You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?

A. The model training dataset

B. The Foundry project display name

C. The exported playground session history

D. The model endpoint and authentication credentials

Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model

Exam Code: AI-901 Q & A: 50 Q&As Updated: May 08,2026

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Exam Code: AI-901
Q & A: 50 Q&As
Updated: May 08,2026

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