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AIF-C01 Practice Question: A data scientist needs to predict whether a…
A data scientist needs to predict whether a transaction is fraudulent (Yes/No). Which type of machine learning problem is this?
⚠ Common exam trap
The AWS AI Practitioner exam often tests the distinction between supervised and unsupervised learning, and the trap here is confusing classification (supervised, discrete output) with clustering (unsupervised, no labels) because both involve grouping or categorizing data.
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
✓
Classification
This is a classification problem because the output is a discrete category (fraudulent or not). The data scientist is predicting a binary label (Yes/No), which is the defining characteristic of binary classification in supervised learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Classification
Why this is correct
Predicting a discrete Yes/No label means the target variable is categorical, which is supervised classification. Regression would output a continuous value, and clustering is unsupervised, so neither fits the fraudulent-or-not constraint. Classification models learn a decision boundary separating the two classes.
- ✗
Clustering
Why it's wrong here
Clustering is unsupervised, grouping unlabelled data by similarity, so it cannot assign the Yes/No fraud label the stem requires. It is tempting because clustering can surface anomalous transaction patterns, but it would be correct only for exploratory segmentation without predefined fraud labels.
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Regression
Why it's wrong here
Regression predicts continuous numeric values, not discrete Yes/No class labels, so it cannot output the fraud classification required. It is tempting because regression and classification share supervised learning, and regression suits predicting transaction amount, but the stem demands a binary category.
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Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent through reward signals from sequential environment interaction, not from a labelled fraud dataset, so it cannot predict Yes/No directly. It is tempting because fraud detection can be framed as sequential decision-making, but supervised classification matches this labelled binary prediction task.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.