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 a continuous numeric value, whereas ticket categories are discrete class labels. It is tempting because regression is supervised and uses labelled historical data, but it would be correct only for predicting quantities such as resolution time or ticket count.
- ✗
Binary classification
Why it's wrong here
Binary classification distinguishes exactly two classes, but the stem requires choosing among several categories such as billing, technical and account. It is tempting because it is supervised classification on labelled data, but it would be correct only for a yes/no outcome such as escalation versus no escalation.
- ✓
Multi-class classification
Why this is correct
Historical tickets carry one label from a set of more than two predefined categories, so the model must assign each ticket to exactly one of several mutually exclusive classes. That maps to multi-class classification, not binary, regression or clustering.
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Clustering
Why it's wrong here
Clustering groups unlabelled data by inferred similarity, so it cannot assign the predefined category names the tickets must receive. It is tempting because it discovers structure in text, but it would be correct only for exploratory segmentation where no historical labels exist.
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