Question 94 of 1,020

Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped

What Type of AI Task Predicts Patient Readmission Risk?

This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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 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?

A
Clustering to group similar patients together
Why wrong: Clustering is unsupervised and finds groups — predicting specific outcomes for individual patients is supervised learning.
B
Classification or regression to predict readmission risk
Readmission prediction is supervised learning — either binary classification (yes/no) or regression (risk score) using patient features.
C
Generative AI to create patient health summaries
Why wrong: Generating summaries is a different task — predicting readmission risk is a predictive classification/regression problem.
D
Anomaly detection to find unusual test results
Why wrong: Anomaly detection flags unusual values — predicting readmission uses patient history to forecast a specific future event.

Full breakdown with real-world context →

Quick Answer

The answer is classification or regression, as predicting patient readmission risk is a supervised learning task that can be framed either way depending on the desired output. In this predictive AI scenario, the model learns from historical patient data—such as age, diagnosis, and lab results—to estimate the likelihood of readmission within 30 days of discharge. If the output is a continuous probability like 0.75, it is regression; if it is a discrete category like high or low risk, it is classification. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between supervised learning subtypes, and a common trap is assuming only one approach is correct—remember that both are valid for this task. A helpful memory tip: think of regression as a “ruler” measuring risk on a scale, and classification as a “label” stamping a risk category.

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.

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.

✗
Clustering to group similar patients together
Why it's wrong here
Clustering is unsupervised and finds groups — predicting specific outcomes for individual patients is supervised learning.
✓
Classification or regression to predict readmission risk
Why this is correct
Readmission prediction is supervised learning — either binary classification (yes/no) or regression (risk score) using patient features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Generative AI to create patient health summaries
Why it's wrong here
Generating summaries is a different task — predicting readmission risk is a predictive classification/regression problem.
✗
Anomaly detection to find unusual test results
Why it's wrong here
Anomaly detection flags unusual values — predicting readmission uses patient history to forecast a specific future event.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates 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.

Detailed technical explanation

How to think about this question

In practice, a hospital might use logistic regression (classification) or a Cox proportional hazards model (regression for time-to-event) to predict 30-day readmission. Key features include length of stay, number of prior admissions, and comorbidity indices like Charlson score. The model's output is often calibrated using Platt scaling to produce well-calibrated probabilities for clinical decision thresholds.

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: 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.

What should I do if I get this AI-900 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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