Practice Questions

---

Question 1

What is the primary goal of privacy in an AI solution?

Answer: To protect personal and sensitive data used by or generated from the AI system.

Explanation: Privacy focuses on how personal data is collected, used, stored, and shared to protect individuals’ rights and expectations.

---

Question 2

Which scenario represents a security concern rather than a privacy concern?

Answer: Unauthorized access to an AI model and its training data.

Explanation: Security focuses on protecting systems, models, and data from unauthorized access, attacks, or misuse.

---

Question 3

An AI-powered chatbot stores users’ conversations, including names and addresses. Which Responsible AI principle is most directly involved?

Answer: Privacy

Explanation: Storing personal information requires careful handling to protect user privacy.

---

Question 4

Which AI workload is most likely to raise privacy concerns?

Answer: Any AI workload that processes personal or sensitive data.

Explanation: Privacy considerations apply across all AI workloads when personal data is involved.

---

Question 5

Why is user consent important in AI solutions?

Answer: Because users should understand and agree to how their data is collected and used.

Explanation: Obtaining consent helps ensure responsible and ethical use of personal data.

---

Question 6

A document processing system analyzes contracts containing confidential information. What is the main concern?

Answer: Protecting sensitive data from unauthorized exposure.

Explanation: Handling confidential documents requires strong privacy and security considerations.

---

Question 7

Which practice supports privacy in an AI solution?

Answer: Collecting only the data necessary for the intended purpose.

Explanation: Limiting data collection reduces privacy risks and potential misuse.

---

Question 8

Why must AI systems be protected against unauthorized access?

Answer: To prevent data breaches, misuse, or manipulation of AI models.

Explanation: Unauthorized access can compromise both the security and trustworthiness of AI systems.

---

Question 9

Which Microsoft framework includes privacy and security as core principles?

Answer: Responsible AI

Explanation: Privacy and security are fundamental principles within Microsoft’s Responsible AI framework.

---

Question 10

An organization wants to ensure its generative AI system does not expose sensitive training data. Which consideration should guide this effort?

Answer: Privacy and security

Explanation: Preventing unintended disclosure of sensitive data is central to protecting privacy and maintaining system security.

---

Exam tip

For AI-900 questions, privacy usually involves personal data and consent, while security involves protecting systems and data from unauthorized access or misuse. Scenario keywords often make the distinction clear.

---

Go to the AI-900 Exam Prep Hub main page.

Practice Questions

---

Question 1

What is the primary goal of inclusiveness in an AI solution?

Answer: To ensure AI systems are usable and beneficial for people with diverse abilities, backgrounds, and needs.

Explanation: Inclusiveness focuses on empowering all users and avoiding designs that unintentionally exclude certain groups.

---

Question 2

Which scenario best represents an inclusiveness concern?

Answer: A speech recognition system that performs poorly for users with strong accents.

Explanation: Failing to support diverse accents and speech patterns limits accessibility and inclusiveness.

---

Question 3

An AI application does not support screen readers or accessibility tools. Which Responsible AI principle is most impacted?

Answer: Inclusiveness

Explanation: Lack of accessibility features can exclude users with disabilities, which is a core inclusiveness issue.

---

Question 4

Which AI workload is most likely to require inclusiveness considerations?

Answer: All AI workloads intended for use by people

Explanation: Inclusiveness applies to any AI system that interacts with or affects users, regardless of workload type.

---

Question 5

Why is it important to test AI systems with diverse user groups?

Answer: To identify usability issues that may affect different abilities, languages, or contexts.

Explanation: Testing with diverse users helps ensure the AI solution works well for a broader population.

---

Question 6

A computer vision system fails to recognize assistive devices such as wheelchairs. What is the main concern?

Answer: The system is not inclusive of users with disabilities.

Explanation: Inclusive AI should consider assistive technologies and diverse physical environments.

---

Question 7

Which practice best supports inclusiveness in AI solutions?

Answer: Designing AI systems that support accessibility standards and tools.

Explanation: Accessibility support helps ensure AI systems can be used by people with varying abilities.

---

Question 8

Why is inclusiveness important for AI systems used by a global audience?

Answer: Because users may have different languages, cultures, abilities, and access needs.

Explanation: Inclusive design helps ensure AI solutions are usable and relevant across diverse populations.

---

Question 9

Which Microsoft framework includes inclusiveness as a core principle?

Answer: Responsible AI

Explanation: Inclusiveness is one of Microsoft’s six Responsible AI principles.

---

Question 10

An organization wants its AI system to avoid assumptions about users’ abilities or environments. Which principle should guide this effort?

Answer: Inclusiveness

Explanation: Inclusiveness encourages designing AI systems that adapt to diverse user needs rather than assuming a single type of user.

---

Exam tip

For AI-900 questions, inclusiveness is usually indicated by scenarios involving accessibility, disabilities, language support, accents, or diverse user needs. When the question is about who can or cannot use the AI system, inclusiveness is often the correct principle.

---

Go to the AI-900 Exam Prep Hub main page.

Overview

Inclusiveness is a key guiding principle of Responsible AI and an important concept on the AI-900: Microsoft Azure AI Fundamentals exam. Inclusiveness focuses on designing AI solutions that empower and benefit all people, including individuals with different abilities, backgrounds, cultures, and access needs.

For the AI-900 exam, candidates are expected to understand what inclusiveness means in the context of AI, recognize inclusive and non-inclusive design scenarios, and identify why inclusiveness is essential for responsible AI solutions.

---

What does inclusiveness mean in AI?

Inclusiveness in AI refers to designing systems that:

• Are usable by people with diverse abilities and needs
• Consider different languages, cultures, and contexts
• Avoid excluding or disadvantaging specific groups
• Provide accessible experiences whenever possible

An inclusive AI solution aims to expand access and opportunity, rather than unintentionally limiting who can benefit from the technology.

---

Why inclusiveness matters

If inclusiveness is not considered, AI systems may:

• Be difficult or impossible for some people to use
• Exclude individuals with disabilities
• Fail to support diverse languages or accents
• Work well only for a narrow group of users

Inclusive AI helps ensure that technology benefits a broader population and does not reinforce existing barriers.

---

Examples of inclusiveness concerns

Common real-world examples include:

• Speech recognition systems that struggle with certain accents or speech patterns
• Computer vision systems that fail to recognize assistive devices such as wheelchairs
• Chatbots or applications that do not support screen readers or accessibility tools
• AI systems that assume all users have the same physical, cognitive, or technical abilities

In each case, the concern is whether the AI solution accommodates diverse user needs.

---

Inclusiveness across AI workloads

Inclusiveness applies across all AI workloads, including:

• Speech AI, ensuring support for different accents, languages, and speech styles
• Computer vision, accounting for varied physical environments and assistive technologies
• Natural language processing, supporting multiple languages and inclusive language use
• Generative AI, producing content that is accessible and usable by diverse audiences

Any AI system intended for broad use should consider inclusiveness.

---

Designing for inclusiveness

While AI-900 does not test technical design methods, it is important to recognize high-level inclusive practices:

• Considering a wide range of users during design
• Supporting accessibility tools and standards
• Testing AI systems with diverse user groups
• Avoiding assumptions about user abilities or contexts

These practices help ensure AI solutions are usable by more people.

---

Microsoft’s approach to inclusiveness

Inclusiveness is one of Microsoft’s Responsible AI principles, emphasizing the importance of designing AI systems that empower people and respect human diversity.

Microsoft encourages building AI solutions that are accessible, adaptable, and beneficial to individuals with varying needs and abilities.

---

Key takeaways for the AI-900 exam

• Inclusiveness focuses on accessibility and diversity
• AI systems should accommodate users with different abilities, languages, and contexts
• Lack of inclusiveness can unintentionally exclude groups of people
• Inclusiveness applies to all AI workloads
• Inclusiveness is a core principle of Microsoft’s Responsible AI framework

---

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

Practice Questions

---

Question 1

What does transparency mean in the context of responsible AI?

A. Ensuring AI models are open source
B. Making AI systems explainable and understandable to users
C. Encrypting all data used by AI systems
D. Ensuring AI decisions are always correct

Correct Answer: B

Explanation:
Transparency focuses on helping users understand how and why an AI system produces its results. This includes explainability, documentation, and clear communication of system capabilities and limitations.

---

Question 2

Why is transparency especially important in AI systems that affect people’s lives?

A. It reduces infrastructure costs
B. It improves model training speed
C. It helps users trust and appropriately rely on AI decisions
D. It guarantees fairness

Correct Answer: C

Explanation:
Transparency builds trust by allowing users to understand AI decisions, especially in sensitive areas like hiring, lending, or healthcare. It does not automatically guarantee fairness, but it supports accountability.

---

Question 3

Which scenario best demonstrates a lack of transparency?

A. A chatbot explains it is an AI system before starting a conversation
B. A loan approval model provides approval probabilities
C. A facial recognition system provides results without explanation
D. A recommendation engine displays confidence scores

Correct Answer: C

Explanation:
Providing results without explanation limits transparency. Users should understand what the system is doing and why, especially when outcomes affect them.

---

Question 4

Which Microsoft Responsible AI principle is most closely associated with explaining AI decisions to users?

A. Fairness
B. Reliability and Safety
C. Transparency
D. Privacy and Security

Correct Answer: C

Explanation:
Transparency focuses on making AI systems understandable, including explanations of decisions, limitations, and confidence levels.

---

Question 5

What is one way to improve transparency in an AI solution?

A. Increase model complexity
B. Hide training data sources
C. Provide explanations and confidence levels
D. Remove human oversight

Correct Answer: C

Explanation:
Providing explanations, confidence scores, and documentation helps users understand how the AI operates and how much to trust its output.

---

Question 6

An AI system is used to classify customer feedback sentiment. Which feature best supports transparency?

A. Faster inference time
B. Clear documentation describing how sentiment is determined
C. Larger training dataset
D. Automatic retraining

Correct Answer: B

Explanation:
Transparency is supported through documentation and explanations, not performance optimizations or automation alone.

---

Question 7

Which of the following is a transparency-related question a user might ask?

A. How quickly does the model run?
B. How was this decision made?
C. How much does the service cost?
D. How often is the model retrained?

Correct Answer: B

Explanation:
Transparency addresses questions about how decisions are made and what factors influence AI outputs.

---

Question 8

Why should AI systems clearly communicate their limitations?

A. To reduce model accuracy
B. To discourage user adoption
C. To help users make informed decisions
D. To meet performance benchmarks

Correct Answer: C

Explanation:
Communicating limitations ensures users do not over-rely on AI systems and understand when human judgment is required.

---

Question 9

Which Azure AI practice supports transparency for end users?

A. Automatically scaling compute resources
B. Logging system errors
C. Providing model explanations and confidence scores
D. Encrypting data at rest

Correct Answer: C

Explanation:
Model explanations and confidence scores directly help users understand AI predictions, supporting transparency.

---

Question 10

How does transparency contribute to responsible AI usage?

A. By removing bias from models
B. By ensuring AI systems never fail
C. By enabling accountability and informed trust
D. By improving training speed

Correct Answer: C

Explanation:
Transparency enables accountability, helps users trust AI appropriately, and supports ethical decision-making. It complements, but does not replace, other principles like fairness or reliability.

---

Go to the AI-900 Exam Prep Hub main page.

Practice Exam Questions

---

Question 1

An organization deploys an AI system that automatically approves or rejects loan applications. To meet Microsoft’s Responsible AI principles, the organization requires employees to review rejected applications when customers appeal a decision.

Which Responsible AI principle does this best demonstrate?

A. Fairness
B. Transparency
C. Accountability
D. Inclusiveness

Correct Answer: C

Explanation:
Accountability ensures that humans remain responsible for AI decisions. Allowing human review and intervention demonstrates human oversight, a core accountability requirement.

---

Question 2

Which action best supports accountability in an AI solution?

A. Encrypting training data
B. Providing explanations for model predictions
C. Assigning a team responsible for monitoring AI behavior
D. Increasing the size of the training dataset

Correct Answer: C

Explanation:
Accountability requires clear ownership and responsibility. Assigning a team to monitor and manage AI outcomes ensures humans are accountable for system behavior.

---

Question 3

An AI-based hiring system logs every candidate ranking decision and allows auditors to review historical outcomes.

Which accountability consideration is being addressed?

A. Human oversight
B. Monitoring and auditing
C. Inclusiveness
D. Data minimization

Correct Answer: B

Explanation:
Logging decisions and enabling audits supports monitoring and auditing, which helps organizations remain accountable for AI behavior over time.

---

Question 4

Which scenario best illustrates a lack of accountability in an AI solution?

A. The AI system provides confidence scores with predictions
B. The organization cannot explain who owns the AI system
C. Training data is encrypted at rest
D. Users are informed that AI is being used

Correct Answer: B

Explanation:
If no one is responsible for the AI system, accountability is missing. Ownership and responsibility are core elements of accountability.

---

Question 5

A healthcare AI solution flags high-risk patients. Final treatment decisions are always made by doctors.

Which concept does this scenario demonstrate?

A. Transparency
B. Fairness
C. Human-in-the-loop accountability
D. Privacy

Correct Answer: C

Explanation:
Human-in-the-loop systems ensure humans make final decisions, reinforcing accountability in high-impact scenarios.

---

Question 6

Which statement best describes accountability in AI?

A. AI systems should never make automated decisions
B. AI models must be open source
C. Humans remain responsible for AI outcomes
D. AI decisions must be unbiased

Correct Answer: C

Explanation:
Accountability means humans and organizations remain responsible, even when AI systems are automated.

---

Question 7

An organization deploys an AI chatbot but ensures complex or sensitive issues are escalated to human agents.

Which Responsible AI principle is primarily demonstrated?

A. Inclusiveness
B. Reliability and safety
C. Accountability
D. Transparency

Correct Answer: C

Explanation:
Escalating decisions to humans ensures human oversight and responsibility, which is central to accountability.

---

Question 8

Which of the following is NOT primarily related to accountability?

A. Audit trails
B. Governance policies
C. Human review processes
D. Data anonymization

Correct Answer: D

Explanation:
Data anonymization relates to privacy, not accountability. The other options ensure human responsibility and oversight.

---

Question 9

After deployment, an AI model’s performance degrades, but no process exists to review or correct its behavior.

Which Responsible AI principle is most at risk?

A. Fairness
B. Accountability
C. Transparency
D. Inclusiveness

Correct Answer: B

Explanation:
Without monitoring or corrective processes, no one is accountable for the AI system’s ongoing behavior.

---

Question 10

On the AI-900 exam, which keyword most strongly indicates an accountability-related question?

A. Encryption
B. Accessibility
C. Ownership
D. Explainability

Correct Answer: C

Explanation:
Ownership is a key indicator of accountability. Accountability questions focus on who is responsible for AI systems and decisions.

---

Exam-Day Tip

If a question mentions:

• Human review
• Oversight
• Governance
• Auditing
• Ownership
• Responsibility

👉 The correct answer might be related to Accountability.

---

Go to the AI-900 Exam Prep Hub main page.