Microsoft Updated AI-901 Exam Questions and Answers by mari

The correct text analysis technique is Named Entity Recognition (NER) .

Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations .

Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.

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

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.

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness .

Why the other options are incorrect:

A. Provide explanations about how predictions are generated = Transparency

C. Evaluate model outputs across demographic groups to reduce bias = Fairness

D. Encrypt stored customer data and restrict access by using role-based controls = Privacy and security