AI-901 Exam Dumps - Microsoft Azure AI Fundamentals (Updated Version)

The described solution coordinates a multi-step workflow : gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI .

Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.

Why the other options are incorrect:

A. information extraction extracts structured data from content.

C. computer vision analyzes images or video.

D. generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI .

Statement 1: To process prompts that contain images, you must deploy a multimodal generative AI model. = Yes

Image prompts require a vision-enabled or multimodal model. A text-only model cannot interpret image content.

Statement 2: In the Foundry playground, you can provide an image by uploading a local file or by specifying a publicly accessible URL. = Yes

The Foundry playground for vision-capable models supports adding image input to a prompt, including local uploads and image URLs.

Statement 3: A multimodal model can interpret multiple images included in a single prompt. = Yes

Vision-enabled multimodal models can accept image content items in a prompt, and multiple images can be included for the model to analyze together.

Statement 1: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format. = No

Azure Content Understanding returns analysis results as structured output, typically JSON , not HTML. Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.

Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No

Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.

Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes

Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.

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.

Tokens per minute, or TPM , is a throughput allocation for a model deployment. Assigning a higher TPM allocation increases the amount of token processing capacity available to the deployment, which affects the speed and scale at which the deployment can process requests.

It does not directly make responses longer, change region availability, or automatically add citations.

To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard .

Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput . It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

Why the other options are incorrect:

A. Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.

C. Compliance relates to governance and compliance, not model cost comparison.

D. Tools provides Foundry tools, not benchmarked cost comparison across models.