AIF-C01 Fundamentals of AI and ML Practice Question
A hospital wants to build a model that predicts whether a patient has a specific disease based on labeled historical medical records where each record is marked either positive or negative. Which type of machine learning problem does this represent?
⚠ Common exam trap
Many candidates confuse labeled binary prediction with regression simply because both are supervised learning tasks.
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 using classification
Because each historical record already includes a known positive or negative label and the target is a discrete category, the task is supervised classification. Regression would predict a continuous value, clustering ignores the labels, and reinforcement learning requires rewards from interaction. Classification directly models the disease outcome the hospital wants to predict.
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 using reward signals
Why it's wrong here
Reinforcement learning trains an agent through trial-and-error interactions with an environment and rewards, which the hospital scenario does not describe. There is no sequential decision process or reward to maximize. The historical labeled records are static examples, so reinforcement learning is the wrong paradigm for predicting a diagnosis.
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Supervised learning using regression
Why it's wrong here
Regression predicts a continuous numeric value such as a price or temperature. Here the outcome is a discrete category, positive or negative, so a regression model would output a number that does not directly represent the diagnosis. Although regression can be adapted, classification is the natural and intended framing for this binary labeled problem.
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Unsupervised learning using clustering
Why it's wrong here
Clustering groups unlabeled data points by similarity, so it does not use the positive and negative labels present in the records. The hospital already knows the outcome for each historical record, so the task is supervised rather than exploratory. Clustering would not produce a model that assigns a disease diagnosis to a new patient based on learned labels.
- ✓
Supervised learning using classification
Why this is correct
The records carry known labels of positive or negative, which is exactly the supervision signal classification uses to learn a decision boundary. Because the target is a discrete category rather than a continuous number, the task is classification. The model can then predict the disease status for new patients, matching the hospital's goal.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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