- A
Classification
Classification predicts categorical labels; here two classes (fraud/not fraud).
- B
Clustering
Why wrong: Clustering is an unsupervised technique for grouping unlabeled data.
- C
Regression
Why wrong: Regression predicts continuous numeric values, not discrete classes.
- D
Reinforcement learning
Why wrong: Reinforcement learning uses an agent that learns from rewards/punishments, not from labeled examples.
Question 96 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 data scientist needs to predict whether a transaction is fraudulent (Yes/No). Which type of machine learning problem is this?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Classification predicts categorical labels; here two classes (fraud/not fraud).
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Clustering
Why it's wrong here
Clustering is an unsupervised technique for grouping unlabeled data.
- ✗
Regression
Why it's wrong here
Regression predicts continuous numeric values, not discrete classes.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses an agent that learns from rewards/punishments, not from labeled examples.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, classification algorithms like logistic regression, decision trees, or support vector machines learn a decision boundary from labeled training data to separate classes. In fraud detection, imbalanced datasets are common, requiring techniques like oversampling (SMOTE) or cost-sensitive learning to avoid a model that always predicts 'not fraudulent' due to class skew.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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