Free Microsoft AI-901 Practice Exam with Questions & Answers | Set: 2

Microsoft’s Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:

auto means the model decides whether to call tools.

required means the model must call one or more tools .

none means the model does not call tools.

Therefore:

A. auto = Incorrect, because the model may or may not call the Azure function.

B. none = Incorrect, because this prevents tool/function calls.

C. required = Correct, because it forces the agent to call a tool.

The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call.

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

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.

Statement 1: An AI generative model is retrained before performing each user request. = No

A generative AI model is not retrained for every user request. During normal use, the model performs inference: it receives input, processes the prompt, and generates a response based on the deployed model.

Statement 2: An AI agent responds by copying and pasting answers stored in a database. = No

An AI agent does not simply copy and paste stored answers. Agents use generative AI models, instructions, context, and optionally tools or connected data sources to reason over user input and produce responses or actions.

Statement 3: An AI agent uses a generative AI model to establish actions based on user input. = Yes

This is correct. An AI agent uses a generative AI model together with instructions and available tools to interpret user input, determine the next action, and generate a response.

For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft’s Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data . It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL .