AIF-C01 Fundamentals of AI and ML Practice Question
A startup needs to predict customer churn based on historical data containing labels (churned or not). Which type of machine learning should they use?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between supervised and unsupervised learning by presenting a scenario with labeled data, where candidates might mistakenly choose unsupervised learning if they overlook the presence of labels.
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
✓
Supervised learning
The startup has labeled historical data (churned or not), which is the defining characteristic of supervised learning. The goal is to learn a mapping from input features to the known output labels to predict churn for new customers. This is a classic classification problem, making supervised learning the correct choice.
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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Reinforcement learning
Why it's wrong here
Reinforcement learning learns policies from reward signals through environment interaction, and cannot consume a fixed labelled churn dataset. It is tempting because it genuinely suits sequential decision problems such as robotics or game playing, but labelled historical records call for supervised classification.
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Unsupervised learning
Why it's wrong here
Unsupervised learning finds structure in unlabelled data, so it cannot map features to the known churned/not-churned labels. It is tempting because it genuinely suits clustering and anomaly detection on unlabelled data, but the presence of historical labels makes supervised classification the correct approach.
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Supervised learning
Why this is correct
Historical data already contains the churn outcome as a label, so the model learns a mapping from input features to that known target. Supervised learning is defined by training on labelled examples, satisfying the labelled churned/not-churned constraint.
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Semi-supervised learning
Why it's wrong here
Semi-supervised learning uses a small labelled dataset to infer labels for a larger unlabelled dataset, but the startup already possesses complete historical labels for every record. The temptation arises because semi-supervised learning is effective when labelling is expensive or scarce, such as in medical imaging where only a few scans are diagnosed. Here, the requirement is supervised learning, which trains directly on the fully labelled churn data to map features to the known binary outcome.
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