AI0-001 Implementing AI Solutions Practice Question
A data science team is training an image classification model for a medical imaging application. To prevent data leakage, they must partition the dataset correctly. Which approach ensures that no patient images appear in both training and test sets?
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
✓
Split by patient ID so that all images of a patient go to one set only
Data leakage occurs when information from the test set leaks into training. Splitting by patient ID ensures that all images from the same patient are kept together in one partition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Split by patient ID so that all images of a patient go to one set only
Why this is correct
Splitting by patient ID ensures no patient appears in both training and test, preventing leakage.
- ✗
Shuffle the dataset and take the first 80% for training and last 20% for testing
Why it's wrong here
If the dataset is not grouped by patient, this method can still split images of the same patient across sets.
- ✗
Use k-fold cross-validation without grouping
Why it's wrong here
Standard cross-validation without grouping also risks leakage from the same patient across folds.
- ✗
Randomly split all images into training and test sets
Why it's wrong here
Random split may place images from the same patient in both sets, causing leakage.
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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.