Microsoft Azure AI Fundamentals Responsible AI 2

Question 1

What is AI bias and why is it a concern in the context of Responsible AI?

Accepted Answer

AI bias occurs when models produce skewed or unfair results due to unrepresentative training data or flawed design

Answer

AI bias arises when training data or model design reflects societal inequities, causing models to produce unfair or discriminatory outcomes for certain groups.

Question 2

Which Azure tool helps teams detect and mitigate fairness issues in machine learning models?

Accepted Answer

Fairlearn (integrated with Azure Machine Learning)

Answer

Fairlearn is an open-source toolkit integrated with Azure Machine Learning that helps assess and mitigate fairness-related harms in machine learning models.

Question 3

In the context of Responsible AI, what is a 'human in the loop'?

Accepted Answer

A person involved in reviewing, overriding, or approving AI decisions to ensure oversight

Answer

Human in the loop refers to a design approach where humans review and can override AI decisions, ensuring meaningful oversight especially in high-stakes scenarios.

Question 4

What does 'model explainability' mean in Responsible AI practice?

Accepted Answer

The capacity to understand and communicate why a model made a specific prediction

Answer

Model explainability refers to techniques that help stakeholders understand why an AI model produced a particular output or decision.

Question 5

Which practice helps ensure AI systems respect user data under the Privacy & Security principle?

Accepted Answer

Implementing data minimization, encryption, and access controls on AI training and inference pipelines

Answer

Privacy & Security best practices include collecting only necessary data, anonymizing training datasets, encrypting data in transit and at rest, and restricting access to sensitive information.

Microsoft Azure AI Fundamentals Practice Test

Microsoft Azure AI Fundamentals Practice Test

Microsoft Azure AI Fundamentals Responsible AI 2