Microsoft Azure AI Fundamentals Machine Learning 4

Question 1

Which approach does Azure Responsible AI use to detect and mitigate bias in ML models?

Accepted Answer

Using the Fairness dashboard and Responsible AI tools to assess and mitigate bias

Answer

Azure's Responsible AI toolkit, including the Fairness dashboard, helps assess model disparity across demographic groups and guides mitigation strategies.

Question 2

What is a 'confusion matrix' used for in machine learning evaluation?

Accepted Answer

Summarizing correct and incorrect predictions across classes for classification models

Answer

A confusion matrix shows true positives, true negatives, false positives, and false negatives, providing a complete picture of a classifier's performance per class.

Question 3

In Azure ML, what is the role of an 'experiment'?

Accepted Answer

A logical container that groups and tracks related training runs

Answer

An Azure ML experiment is a named grouping of runs, allowing you to organize, compare, and track the results of multiple model training iterations.

Question 4

Which data split ratio is most commonly used for dividing data into training and test sets in Azure AutoML's default behavior?

Accepted Answer

70–80% training / 20–30% test

Answer

A 70–80% training and 20–30% test split is the industry standard and Azure AutoML's default approach to balance learning opportunity with evaluation robustness.

Question 5

What does RMSE (Root Mean Squared Error) measure in a regression model?

Accepted Answer

The average magnitude of prediction errors, giving more weight to large errors

Answer

RMSE measures the square root of the average squared differences between predicted and actual values, penalizing larger errors more heavily than MAE.

Microsoft Azure AI Fundamentals Practice Test

Microsoft Azure AI Fundamentals Practice Test

Microsoft Azure AI Fundamentals Machine Learning 4