AI AI Engineer: AI System Design and Ethics 3

Question 1

What is 'model governance' in enterprise AI system design?

Accepted Answer

Policies, controls, and oversight processes that manage the AI model lifecycle from development to retirement

Answer

Model governance encompasses approval workflows, audit trails, risk assessments, and accountability structures ensuring AI models are developed and used responsibly.

Question 2

What is 'adversarial robustness' in AI system design?

Accepted Answer

A model's ability to maintain correct behavior when inputs are deliberately manipulated to cause errors

Answer

Adversarial robustness measures how well a model resists adversarial examples — subtly perturbed inputs crafted to fool the model while appearing normal to humans.

Question 3

What is the primary challenge addressed by 'federated learning' in AI?

Accepted Answer

Training AI models across decentralized devices without sharing raw data, preserving privacy

Answer

Federated learning trains models locally on each device and aggregates only model updates (not raw data), enabling collaboration without centralizing sensitive data.

Question 4

What is 'AI red-teaming'?

Accepted Answer

Adversarial testing where experts try to find failures, vulnerabilities, and harmful behaviors in AI systems

Answer

AI red-teaming involves deliberately probing a system for safety failures, harmful outputs, or exploitable behaviors before deployment, mimicking adversarial users.

Question 5

What does 'scalable oversight' aim to solve in AI alignment research?

Accepted Answer

Maintaining meaningful human supervision of AI as systems become more capable than the humans overseeing them

Answer

Scalable oversight addresses how to keep humans in meaningful control of AI decisions when the AI may eventually be more capable than the humans evaluating it.

AI - Engineer Practice Test

AI - Engineer Practice Test

AI AI Engineer: AI System Design and Ethics 3