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

To return important topics from unstructured text by using Azure Language SDK, use:

client.extract_key_phrases()

Microsoft states that key phrase extraction evaluates unstructured text and returns a list of key phrases or main topics. The Azure Text Analytics client library for Python includes the extract_key_phrases method for this task.

Why the other options are incorrect:

A. client.recognize_linked_entities() identifies entities and links them to a knowledge base.

B. client.recognize_entities() identifies named entities such as people, organizations, locations, dates, and quantities.

C. client.begin_extract_summary() starts an extractive summarization operation, not key phrase extraction.

GPT and DALL-E are examples of generative AI model types/families.

GPT models generate natural language text, such as answers, summaries, emails, and conversations.

DALL-E models generate images from text descriptions.

Why the other options are incorrect:

A. transformer is a model architecture, not the best answer here as a generative model type/family.

B. computer vision is an AI workload area.

E. natural language processing is an AI workload area, not a specific generative AI model type.

Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.

The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.

Why the other options are incorrect:

Human oversight and control aligns more closely with accountability.

Data governance and management aligns more closely with privacy and security.

The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.

“AI systems must be understandable” is an example of the Microsoft responsible AI principle of transparency. Microsoft’s responsible AI framework includes fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

The other options are incorrect: AI systems should not merely protect company interests, should not be limited only to public data, and must not keep personal details public.

Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft’s Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().

Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:

result = poller.result()

Option B is incorrect because extraction results are not read from request headers.

Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.

Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.

For a vision-enabled model, Microsoft documentation shows that multimodal prompts can include both text and image content in the same user message content array. Azure OpenAI vision models accept multimodal image-and-text input and return natural language responses.

The correct request structure is a user message with a content array containing both items, for example:

{

" role " : " user " ,

" content " : [

{ " type " : " input_text " , " text " : " What is in this image? " },

{ " type " : " input_image " , " image_url " : image_url}

]

}

This avoids sending separate requests and is therefore the best choice for quickest response time.

A and C are incorrect because they send the text and image separately.

B is incorrect because the image/question should be sent as a user message, while system messages are used for instructions and behavior.

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.

Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No

Fine-tuning is optional. Microsoft’s Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.

Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No

Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project, and Foundry playgrounds are used for prototyping and validation before production. Therefore, the statement using “only after you deploy” is too restrictive.

Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No

Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy.

Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes

When a deployed generative AI model receives a user prompt and generates an output, that process is called inference. The model is being used to produce a response, not being trained.

Statement 2: A generative AI model generates responses by copying stored documents directly from the model’s training data. = No

Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.

Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model’s training data. = Yes

This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.