AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A real estate company has a dataset containing square footage, number of bedrooms, and location for 10,000 houses, along with their sale prices. They want to train a model that predicts the sale price of a new house based on these features. Which type of machine learning should they use?
⚠ Common exam trap
Test-takers frequently confuse 'classification' with any prediction task, forgetting that regression is specifically for continuous numeric outputs, not categorical labels.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Supervised regression
The goal is to predict a continuous numeric value (sale price) from input features (square footage, bedrooms, location). This is a classic supervised regression problem because the training data includes labeled target values (prices) and the output is a real number, not a category.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Supervised classification
Why it's wrong here
Classification predicts a categorical outcome from a finite set of discrete classes, such as 'will sell' versus 'will not sell' or a quality rating tier. A sale price like $450,000 is a continuous numeric amount, so forcing it into bins would discard precision and cannot represent the full range of possible values. Therefore, supervised classification is not suitable for this real estate prediction task.
- ✓
Supervised regression
Why this is correct
Regression is the correct supervised learning approach because the dataset pairs input features—square footage and other home attributes—with known sale prices, which are continuous numeric labels. The model learns a function that maps features to a dollar amount, allowing the company to predict the sale price of a new property. This task falls under supervised regression because each training record has a ground-truth target value.
- ✗
Unsupervised clustering
Why it's wrong here
Unsupervised clustering works only with input features and has no labeled target variable, so it would discover groups of similar properties based on square footage rather than predict a price. Because the dataset includes known sale prices that act as labels, the problem is fundamentally supervised. Clustering could answer a different question, such as 'which properties form market segments?', but it cannot directly produce continuous price predictions.
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Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent to take sequential actions in an environment and learn from rewards or penalties, which is suited to dynamic domains like robotics or game playing. Predicting a home's sale price from static tabular data involves no environment, no actions, and no delayed feedback that the agent must optimize. Thus, reinforcement learning is irrelevant to this supervised, single-shot regression problem.
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Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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