Machine Learning & Supervised Learning — AI-900 Practice Question
A representative Microsoft Azure AI Fundamentals (AI-900) exam question on Machine Learning & Supervised Learning. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is B. Regression.
Regression is used when predicting continuous numerical values, such as house prices. The model learns the relationship between input features (square footage, bedrooms, location) and output values (price). Classification (A) predicts discrete categories. Clustering (C) groups similar data without labels. Reinforcement Learning (D) learns through trial and error with rewards.
The Question
A real estate company wants to build a model that predicts house prices based on features like square footage, number of bedrooms, and location. Which type of machine learning task is this?
Why B is correct
Regression is used when predicting continuous numerical values, such as house prices. The model learns the relationship between input features (square footage, bedrooms, location) and output values (price). Classification (A) predicts discrete categories. Clustering (C) groups similar data without labels. Reinforcement Learning (D) learns through trial and error with rewards.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (B) is the only one that fully meets every constraint stated in the question.
Key idea: Machine Learning & Supervised Learning
Regression is used when predicting continuous numerical values, such as house prices. The model learns the relationship between input features (square footage, bedrooms, location) and output values (price). Classification (A) predicts discrete categories. Clustering (C) groups similar data without labels. Reinforcement Learning (D) learns through trial and error with rewards. On the AI-900 exam, questions in the "Machine Learning on Azure" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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