AI0-001 Machine Learning and Deep Learning Practice Question
A healthcare analytics team trains a model to flag patients at risk of readmission. The dataset contains 9,500 non-readmitted patients and 500 readmitted patients. The model predicts the majority class for every patient and reports 95 percent accuracy, yet it identifies no at-risk patients. Which evaluation approach best reveals the model's failure?
⚠ Common exam trap
The trap here is trusting a high accuracy number on an imbalanced dataset, when accuracy can be maximized by simply ignoring the minority class that the model exists to detect.
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
✓
Compute the confusion matrix and examine recall and precision for the readmission class
With a 95-to-5 class split, a majority-class predictor achieves 95 percent accuracy while providing zero clinical value. The confusion matrix and minority-class recall and precision expose the absence of true positives, which is the evidence needed to justify resampling, class weighting, or threshold tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Measure training time and inference latency across the full dataset
Why it's wrong here
Runtime and latency describe operational efficiency, not predictive quality. A model can be extremely fast while predicting the majority class for every patient, so these measurements provide no evidence about whether at-risk patients are being identified and cannot diagnose the imbalance-driven failure.
- ✓
Compute the confusion matrix and examine recall and precision for the readmission class
Why this is correct
The confusion matrix exposes the zero true positives directly, and recall for the readmission class is zero while precision is undefined. These class-specific metrics reveal that the model catches no at-risk patients, which accuracy hides. Recall matters most clinically because a missed readmission is a false negative with real patient harm.
- ✗
Report overall accuracy on a stratified holdout set
Why it's wrong here
Overall accuracy is exactly the metric that produced the misleading 95 percent figure, because predicting the majority class for everyone yields the same score as the class prevalence. On this imbalanced dataset, accuracy cannot distinguish a useful classifier from a degenerate one that never flags the minority readmission class.
- ✗
Calculate the mean squared error between predicted probabilities and labels
Why it's wrong here
Mean squared error on hard class predictions reduces to the same misclassification rate reflected by accuracy, so it inherits the identical blind spot. Even with probabilities, the metric aggregates across classes and remains dominated by the 9,500 majority examples, masking the complete failure on the 500 readmitted patients.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.