Quick Answer
The answer is multi-class classification, because the model must assign each support ticket to exactly one of three or more predefined categories like billing, technical, or account. This is a supervised learning problem since the dataset contains labeled historical tickets, and the output is a discrete class label chosen from a set of more than two categories, which distinguishes it from binary classification. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your ability to differentiate between classification types based on the number of output classes; a common trap is confusing multi-class with multi-label classification, where a single ticket could belong to multiple categories simultaneously. Remember the memory tip: "three or more, one score" — multi-class means each instance gets a single label from three or more possible classes.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Regression
Why wrong: Regression predicts continuous numeric values, not categories.
Binary classification
Why wrong: Binary classification has only two classes, but here there are multiple categories.
Multi-class classification
The problem involves predicting one of several discrete categories using labeled training data.
Clustering
Why wrong: Clustering is an unsupervised learning task that groups data without predefined labels.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Clustering
Why it's wrong here
Clustering is an unsupervised learning task that groups data without predefined labels.
Common exam traps
Cisco 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.
Detailed technical explanation
In multi-class classification, the model typically outputs a probability distribution over all classes using a softmax activation function in the output layer, which ensures the sum of probabilities equals 1. Common algorithms include multinomial logistic regression, decision trees, or neural networks with cross-entropy loss. A real-world nuance is class imbalance—if 'billing' tickets vastly outnumber 'technical' tickets, the model may become biased, requiring techniques like weighted loss functions or oversampling.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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