AI0-001 Implementing AI Solutions Practice Question
A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?
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
✓
Train/test leakage caused by using the same data for feature engineering and model selection
Reusing the same data for feature engineering and model selection creates data leakage from validation set into training decisions, leading to overfitting and poor 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.
- ✓
Train/test leakage caused by using the same data for feature engineering and model selection
Why this is correct
Using validation data to guide feature engineering leaks information, inflating training performance and hurting generalization.
- ✗
The dataset is too small for the model complexity
Why it's wrong here
Small data can cause overfitting, but the described workflow strongly indicates leakage as the primary issue.
- ✗
The learning rate is too high
Why it's wrong here
High learning rate would cause divergence, not large train-validation gap after convergence.
- ✗
The model is overfitting due to high variance
Why it's wrong here
Overfitting is a symptom; the root cause here is leakage from improper data splitting practices.
About these practice questions
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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.