AI0-001 Implementing AI Solutions Practice Question
A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?
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 data into training and test sets before any preprocessing
Splitting data before any preprocessing prevents leakage from test set into training. Cross-validation provides a more robust estimate of generalization than a single hold-out set.
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 data into training and test sets before any preprocessing
Why this is correct
Splitting first avoids leaking test information into scaling or imputation.
- ✗
Normalize all features using the entire dataset's statistics
Why it's wrong here
Normalization should be fit on training data only; using entire dataset leaks test information.
- ✓
Use cross-validation to tune hyperparameters
Why this is correct
Cross-validation reduces variance in performance estimates and helps avoid overfitting to a particular split.
- ✗
Remove outliers based on the full dataset distribution
Why it's wrong here
Outlier removal decisions should be made on training data to avoid leakage.
- ✗
Encode categorical variables with target encoding on the full dataset
Why it's wrong here
Target encoding on full dataset leaks target information from test into training.
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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.