Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital wants to use AI to predict which patients are at high risk of readmission within 30 days of discharge. What type of AI task is this?
⚠ Common exam trap
Many exam-takers confuse 'clustering' (unsupervised grouping) with 'classification' (supervised prediction of a known category), especially when the question mentions 'grouping similar patients' — but the goal is to predict a specific outcome, not to discover natural groupings.
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 or regression to predict readmission risk
Predicting readmission risk is a supervised learning task where the model learns from historical patient data (features like age, diagnosis, lab results) to output a risk score. If the output is a continuous probability (e.g., 0.75 risk), it is regression; if it is a discrete category (e.g., high/low risk), it is classification. Both are valid approaches for this predictive scenario.
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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Clustering to group similar patients together
Why it's wrong here
Clustering algorithms like k-means group patients into cohesive segments based on feature similarity without using any label information about who was actually readmitted. This is an unsupervised exploratory technique that reveals population structure, but it cannot assign a quantitative readmission probability to a new patient. To predict readmission, you need a supervised algorithm that learns a mapping from features to known outcomes.
- ✓
Classification or regression to predict readmission risk
Why this is correct
Readmission risk is a supervised learning problem because you train on historical records where the outcome is already known. If the target is a binary label (readmitted within 30 days vs. not), you use binary classification; if the target is a duration or a risk score, you use regression. The model learns patterns from patient features like diagnoses, lab values, and prior admissions to predict an individual patient's future outcome.
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Generative AI to create patient health summaries
Why it's wrong here
Generative AI models like large language models produce new text, such as patient health summaries or discharge notes, from input data. That is a text-generation task, not a predictive one — it does not estimate the probability of a future readmission event. Predicting readmission risk requires a supervised model trained on labeled outcomes, not a model that synthesizes prose.
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Anomaly detection to find unusual test results
Why it's wrong here
Anomaly detection identifies data points that deviate from a normal pattern, such as unusual lab results or rare patient readings, and is often unsupervised because it does not require pre-labeled readmission outcomes. Its goal is to flag outliers, not to forecast a specific clinical event for an individual. Readmission prediction instead leverages historical labels of whether patients were actually readmitted to train a classification or regression model.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Supervised learning
Supervised learning is a type of machine learning where a model learns from labeled training data to make predictions or decisions.
Key term
Regression
Regression is a type of machine learning algorithm that predicts a continuous numeric output based on input data, used to model relationships between variables.
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