AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for training a customer churn prediction model. To prevent train/test leakage, which TWO practices should be followed? (Select TWO)
⚠ Common exam trap
The trap is thinking that shuffling or normalizing on the full dataset is harmless — candidates often pick random shuffle or global normalization, not realizing these leak test set information into training.
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 the data chronologically (e.g., use data before a certain date for training, after for testing)
Option C is correct because splitting data chronologically (e.g., training on records before a cutoff date and testing on records after that date) respects the temporal order of observations and prevents future information from leaking into the training set, which is essential for time-dependent churn prediction. Option E is correct because feature selection must be performed using only the training data; if the test set influences which features are selected, information from the test set leaks into model development and produces overly optimistic performance estimates. Option A is incorrect because removing duplicates only from the test set does not prevent leakage and can distort the test distribution; duplicate handling should be consistent and decided before splitting. Option B is incorrect because random shuffling of the entire dataset before splitting can mix past and future observations, which is especially harmful for temporal churn data and does not by itself prevent leakage. Option D is incorrect because computing normalization statistics on the entire dataset before splitting leaks test-set distribution information into training; normalization statistics must be computed only on the training set and then applied to the test 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.
- ✗
Remove duplicate records only from the test set to ensure uniqueness
Why it's wrong here
Removing duplicates only from the test set could introduce bias; deduplication should be done consistently across the whole dataset.
- ✗
Shuffle the entire dataset randomly before splitting into train and test sets
Why it's wrong here
Random shuffling before splitting can cause leakage if the data has temporal dependencies; it ignores time order.
- ✓
Split the data chronologically (e.g., use data before a certain date for training, after for testing)
Why this is correct
Chronological splitting trains on earlier records and tests on later ones, mirroring real deployment where future data is unseen. This prevents temporal leakage, satisfying the constraint that test data must not influence or overlap with training information.
- ✗
Normalize numerical features using statistics computed on the entire dataset before splitting
Why it's wrong here
Normalizing on the full dataset leaks information from the test set into the training set; normalization should be fit on training data only.
- ✓
Perform feature selection using only the training data, then apply the same features to the test set
Why this is correct
Selecting features on the training set alone prevents test-set information leaking into model choices. Fitting the selector on all data lets the test set influence which features survive, inflating reported performance. The constraint is avoiding train/test leakage, so the selector must see only training rows.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.