Study with AI-901 Actual Test Questions

Correct Answer: A  🗳️

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Correct Answer:

Explanation:

The Model catalog is used for comparing and deploying a wide range of models for generative AI development in Microsoft Foundry.
Microsoft documentation states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications. It also includes many models across providers such as Azure OpenAI, Mistral, Meta, Cohere, NVIDIA, and Hugging Face.
The Microsoft Learn module for Foundry models also states that the model catalog is used to explore and filter models, compare models using benchmark metrics, and deploy a model to an endpoint.

Correct Answer: D  🗳️

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Correct Answer:

Explanation:

input=[
{
" role " : " user " ,
" content " : [
{ " type " : " input_text " , " text " : " What is in this image? Provide 3 bullet points. " },
{ " type " : " input_image " , " image_url " : image_url},
],
}
]
print(response.output_text)
For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: " input_text " , and image content is sent with type: " input_image " . Microsoft's Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.
The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model's text answer.

Correct Answer:

Explanation:
Statement
Answer
Defining an AI system for a typical user profile is sufficient to support inclusiveness.
No
A high number of active users means an AI system provides an inclusive experience.
No
Providing accessible interfaces and language options helps support inclusiveness in an AI system.
Yes
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is No, No, Yes.
Microsoft defines inclusiveness as designing AI systems so they work effectively for the full range of people they are intended to serve. The current AI-901 study guide explicitly includes the ability to describe considerations for inclusiveness in an AI solution as part of the Responsible AI objective domain.
The first statement is No because designing only for a hypothetical or "typical" user can exclude people with different abilities, languages, interaction styles, cultural backgrounds, or accessibility requirements. Inclusive design deliberately considers diverse and potentially underserved users.
The second statement is No because the number of active users is an adoption or usage metric. A system may have millions of users while still creating accessibility barriers or excluding particular groups. Usage volume therefore does not demonstrate inclusiveness.
The third statement is Yes. Accessible interfaces, assistive-technology compatibility, and suitable language options reduce barriers and broaden the range of people who can effectively interact with an AI system.
Microsoft specifically emphasizes accessibility and supporting varied user needs as core elements of inclusive AI design.