Practice Questions

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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