AIF-C01 Fundamentals of AI and ML Practice Question
A financial institution wants to predict whether a loan applicant will default. They have a historical dataset with loan outcomes (default or no default) and various applicant features. Which type of machine learning should they use?
⚠ Common exam trap
A common mix-up: candidates confuse clustering with classification because both group data, but clustering is unsupervised and cannot predict a labeled outcome.
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
Supervised learning is ideal when historical labeled data is available and the goal is to predict a known outcome. The loan default prediction task is a binary classification problem, which supervised learning handles by learning from past examples to classify new applicants.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning learns through trial and error by interacting with an environment. Loan default prediction is a static classification problem, not a sequential decision-making task. There is no reward mechanism or dynamic environment, so reinforcement learning is not appropriate.
- ✗
Unsupervised learning
Why it's wrong here
Unsupervised learning is used for unlabeled data to find hidden patterns. Since the dataset includes known outcomes (default or no default), it is labeled, making unsupervised learning unsuitable. Clustering applicants would not directly predict default risk.
- ✗
Clustering
Why it's wrong here
Clustering is an unsupervised technique that groups similar data points without using labels. It cannot directly predict a binary outcome like default. While clustering could segment applicants, it does not provide a predictive model for default risk.
- ✓
Supervised learning
Why this is correct
Supervised learning uses labeled data to train a model that maps input features to a target output. Here, the target is binary (default or no default), and historical labeled data is available. This allows training a classification model to predict default for new applicants, making supervised learning the correct choice.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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