Practice Free AI-901 Microsoft Azure AI Fundamentals (Updated Version) Exam Questions Answers With Explanation

The completed sentence is:

Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability .

Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.

The other options are incorrect:

fairness focuses on avoiding bias and treating people fairly.

privacy and security focuses on protecting data and restricting access.

transparency focuses on explaining AI use, capabilities, and limitations.

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.

Azure Content Understanding uses transcription to convert audio content, such as voicemail recordings, into text. Microsoft’s Azure Content Understanding audio documentation states that transcription converts conversational audio into searchable and analyzable text-based transcripts.

Option A. Voice Live is not the feature used by Content Understanding to convert voicemail recordings into text.

Option B. key phrase extraction identifies important phrases after text is available; it is not the audio-to-text conversion step.

Option D. optical character recognition (OCR) is for extracting text from images or documents, not audio.

Therefore, the correct answer is C. transcription .

Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness .

The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.

The other options are incorrect:

accountability means people and organizations remain responsible for AI systems and their effects.

privacy and security focuses on protecting data and controlling access.

transparency means users should understand when and how AI is being used, including system capabilities and limitations.

The completed sentence is:

An AI workload that produces new content based on user input is an example of generative AI .

Generative AI is used to create new content, such as text, images, code, audio, or other outputs, from user prompts or instructions.

The other options are incorrect:

content understanding extracts or interprets information from existing content.

information extraction identifies and extracts structured data from content.

text analysis analyzes existing text for entities, sentiment, key phrases, language, or summaries.

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.

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.

To create an AI agent that follows a specific role and behavior, you configure system instructions . Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior .

Option A. tokens per minute (TPM) controls throughput quota, not behavior.

Option C. temperature controls response randomness/creativity, not the agent’s role.

Option D. max completion tokens controls response length, not the agent’s role or behavioral rules.

Therefore, the correct answer is B. system instructions .

The completed sentence is:

After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model .

To call a deployed model from an application, the application must send requests to the deployed model’s endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.

The other options are incorrect:

evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.

Foundry playground is an interactive testing environment, not the production target for application requests.

training dataset of the model is used for training or fine-tuning, not for sending inference requests.

Therefore, the correct answer is endpoint of the model .

A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.

Statement 1: System prompts can be used to authorize users. = No

Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.

Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No

TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.

Statement 3: A system prompt guides the behavior of a generative AI model. = Yes

This is correct. Microsoft documentation says system messages help steer the model’s behavior, tone, and output format.