Free Practice Questions for Microsoft AI-901 Exam

Verifying that machine learning models do not show racial or gender bias is an example of the Microsoft responsible AI principle of fairness.

Fairness means AI systems should treat people equitably and avoid unfair bias across demographic groups such as race, gender, age, or other protected characteristics.

Why the other options are incorrect:

A. Safety focuses on avoiding harm and operating safely.

B. Privacy and security focuses on protecting data and access.

C. Reliability focuses on consistent and dependable operation.

D. Fairness directly addresses bias and equitable treatment.

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.

For a vision-enabled GPT model deployment, use the Azure OpenAI Responses API to send text and image inputs in a single request. Microsoft’s Foundry SDK documentation states that the endpoint exposes the Responses API and provides access to Azure OpenAI models, including chat completions and multimodal-capable model interactions.

The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.

Why the other options are incorrect:

B. image analysis is used to examine and interpret existing images.

C. object detection is used to identify and locate objects within an existing image.

D. optical character recognition (OCR) is used to extract text from images or scanned documents.

Since the solution must generate new visual content from user-provided descriptions, the correct answer is:

A. image generation

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.

Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user’s prompt.

B. computer vision is for interpreting images or video.

C. text classification categorizes existing text, but does not draft new marketing emails.

D. speech recognition converts spoken audio into text.

Therefore, the correct answer is A. generative AI.

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.

Fairness

Privacy and security

Transparency

Reliability and safety

Comprehensive and Detailed Explanation with all Azure AI documents: =

Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups: Fairness

Microsoft’s responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.

Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security

The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.

Informing users when they are interacting with an AI system and explaining the system’s capabilities and limitations: Transparency

The transparency principle requires that people understand when they are interacting with AI and understand the system’s purpose, capabilities, and limitations.

Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety

The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.