Pass4Future also provide interactive practice exam software for preparing Microsoft Azure AI Fundamentals (Updated Version) (AI-901) Exam effectively. You are welcome to explore sample free Microsoft AI-901 Exam questions below and also try Microsoft AI-901 Exam practice test software.
Do you know that you can access more real Microsoft AI-901 exam questions via Premium Access? ()
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer : C, D
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.
You need to convert written customer notifications into natural-sounding spoken audio that can be played over a phone system.
Which Azure Speech in Foundry Tools capability should you use?
Answer : B
The requirement is to convert written customer notifications into natural-sounding spoken audio. This is speech synthesis, also known as text to speech.
Microsoft's Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis.
Why the other options are incorrect:
A . speaker recognition identifies or verifies speakers by voice. C . speech recognition converts spoken audio into text. D . speech translation translates spoken audio between languages.
You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?
Answer : C
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.
You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.
What should you provision?
Answer : B
To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.
The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.
Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?
Answer : D
When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:
{'type': 'input_image', 'image_url': image_url}
Microsoft's Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.
Why the other options are incorrect:
A . image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image. B . image_generation = Incorrect. This is related to generating images, not providing an image for analysis. C . output_image = Incorrect. This would refer to generated output, not image input. D . input_image = Correct.
