AI0-001 AI Concepts and Foundations Practice Question
An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and dataset shift, where candidates mistakenly attribute a deployment performance drop to overfitting even when test accuracy is high, missing the real issue of distribution mismatch.
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
✓
The training data does not represent the new hospital's population or imaging equipment.
The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is being attacked by adversarial examples.
Why it's wrong here
Adversarial examples require deliberately perturbed inputs, whereas a routine site change causes the drop. It is tempting because adversarial attacks do degrade deployed models, and would be correct if inputs were intentionally manipulated to induce misclassification.
- ✓
The training data does not represent the new hospital's population or imaging equipment.
Why this is correct
Distribution shift explains the drop: the model learned patterns tied to the original hospital's patient demographics and scanner characteristics, so features generalise poorly to different equipment and populations. This directly satisfies the stem's cross-hospital deployment constraint, where 99% test accuracy reflects only in-distribution performance.
- ✗
The model is overfitted to the training data.
Why it's wrong here
Overfitting would already show poor test-set performance before deployment, yet the model scored 99% there. It is tempting because overfitting is the classic cause of generalisation failure, and would be correct if the held-out test set itself had scored poorly.
- ✗
Data leakage occurred during preprocessing.
Why it's wrong here
Data leakage inflates validation scores during development, but the stem reports a genuine 99% test result, so leakage is not evidenced. It is tempting because leakage does cause deployment surprises, and would be correct if preprocessing had fitted on data including test samples.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.