AIF-C01 Fundamentals of AI and ML Practice Question
A company wants to build a system that automatically categorizes customer support tickets into predefined categories (e.g., billing, technical, account). The team has a large dataset of historical tickets with their category labels. Which type of machine learning problem is this?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between binary and multi-class classification by presenting a scenario with multiple categories but implying a simple yes/no decision, leading candidates to mistakenly choose binary classification when the number of classes exceeds two.
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
✓
Multi-class classification
This is a multi-class classification problem because the model must assign each support ticket to one of three or more predefined categories (e.g., billing, technical, account). The dataset provides labeled historical tickets, making it a supervised learning task, and the output is a discrete class label from a set of more than two categories, which distinguishes it from binary classification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Regression
Why it's wrong here
Regression predicts continuous numeric values, not categories.
- ✗
Binary classification
Why it's wrong here
Binary classification has only two classes, but here there are multiple categories.
- ✓
Multi-class classification
Why this is correct
The problem involves predicting one of several discrete categories using labeled training data.
- ✗
Clustering
Why it's wrong here
Clustering is an unsupervised learning task that groups data without predefined labels.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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