AI0-001 Implementing AI Solutions Practice Question
A data science team is preparing a dataset for a supervised learning task. They split the data into training and test sets. The team then normalizes the features using the mean and standard deviation calculated from the entire dataset before splitting. What issue does this introduce?
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
✓
It introduces train/test leakage
Using statistics from the entire dataset before splitting causes test data information to influence the training process, leading to train/test leakage and overly optimistic performance estimates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It improves model generalization
Why it's wrong here
This practice actually harms generalization due to leakage, not improves it.
- ✓
It introduces train/test leakage
Why this is correct
Correct: normalizing using global statistics means test set information is used to transform training data, leaking information.
- ✗
It causes the model to overfit the training data
Why it's wrong here
Overfitting is possible but not directly caused by this normalization order; leakage is the primary issue.
- ✗
It reduces the variance of the features
Why it's wrong here
Normalization changes variance but that is not the main problem; the issue is information 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.