easyMultiple Choice
AIF-C01 Practice Question: A data scientist needs to predict house prices…
A data scientist needs to predict house prices based on features like square footage, number of bedrooms, and location. Which type of machine learning is most appropriate for this task?
⚠ Common exam trap
AWS AI Practitioner exams often test the distinction between regression and classification by presenting a prediction task with a continuous output, where candidates mistakenly choose classification because they focus on the word 'predict' without recognizing the output type.
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
✓
Regression
Regression is the correct choice because the task involves predicting a continuous numerical value (house price) based on input features. Unlike classification, which predicts discrete categories, regression models the relationship between independent variables and a continuous target, making it ideal for price prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Classification
Why it's wrong here
Classification predicts discrete class labels, whereas house prices are continuous numeric values, so the model would need regression. It is tempting because supervised learning fits both tasks and classification is often the first technique taught, and it would be correct for predicting a category such as sold or unsold.
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Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent through reward signals from sequential actions, but house-price prediction is a single supervised mapping from features to a continuous value. It is tempting because it handles complex decision problems, and it would be correct for tasks such as dynamic pricing or bidding over time.
- ✓
Regression
Why this is correct
Regression models a continuous numeric target, so it predicts a price value from features such as square footage, bedrooms and location. Classification would output discrete labels, which cannot represent the continuous house-price output the scenario requires.
- ✗
Clustering
Why it's wrong here
Clustering is unsupervised and groups unlabelled records by similarity, so it cannot use the known prices as targets to predict a value. It is tempting because it also works with feature data and reveals market segments, and it would be correct for segmenting neighbourhoods or buyer groups without labels.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.