AI0-001 AI Concepts and Techniques Practice Question
A data scientist needs to predict whether a customer will churn (yes/no) based on historical data. Which type of machine learning problem is this?
⚠ Common exam trap
CompTIA often tests the distinction between classification and regression by presenting a binary outcome and expecting candidates to recognize it as classification, not regression, even though the term 'regression' appears in 'logistic regression' which is actually a classification algorithm.
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
✓
Binary classification
This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.
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 trains an agent through reward signals from interacting with an environment, producing sequential policies rather than fixed labels. It suits control and decision-making tasks, whereas predicting a binary churn outcome from historical records is supervised classification, not reward-driven sequential learning.
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Regression
Why it's wrong here
Regression predicts continuous numeric values, so it cannot output a discrete yes/no class label. It would be correct for forecasting churn probability as a number or predicting revenue, but classifying customers into churn or no-churn categories is binary classification, which regression does not perform.
- ✓
Binary classification
Why this is correct
Churn prediction produces one of two discrete outcomes, yes or no, so the target variable is binary. Binary classification is the problem type defined by exactly two mutually exclusive class labels, matching the stem's yes/no requirement.
- ✗
Clustering
Why it's wrong here
Clustering groups unlabelled records by similarity, so it cannot map inputs to the yes/no churn labels the scenario requires. It is tempting because it also works on historical customer data, and it would be correct for segmenting customers into undiscovered groups without predefined outcomes.
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