AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

Question: 1 / 400

Which is NOT a characteristic of supervised learning?

Requires labeled data

Used for clustering

In supervised learning, the model is trained using labeled data, meaning that each training example comes with a corresponding output label. This characteristic allows the model to learn the mapping from inputs to outputs, making it suitable for prediction tasks, where the goal is to forecast outcomes based on new, unseen input data. Evaluation in supervised learning typically involves assessing the model's accuracy, meaning it measures how well the model's predictions match the actual labels in the test data.

Clustering, on the other hand, is an unsupervised learning technique where the goal is to group similar data points together without any labeled outputs. Since there is no designated output in clustering, this method is not characteristic of supervised learning. Therefore, indicating that supervised learning is used for clustering correctly identifies a difference between the two paradigms.

Get further explanation with Examzify DeepDiveBeta

Typical for prediction tasks

Evaluation based on accuracy metrics

Next Question

Report this question

Subscribe

Get the latest from Examzify

You can unsubscribe at any time. Read our privacy policy