hardMultiple Choice
MLA-C01 Time-based split Practice Question
An ML team is building a time-series forecasting model for daily sales. They need to split the data into training and validation sets without data leakage, and the validation set should be the most recent 30 days. Which splitting strategy should they use?
⚠ Common exam trap
MLA-C01 often tests whether candidates recognize that any random or stratified split introduces temporal data leakage in forecasting problems — the trap is picking k-fold because it sounds more rigorous.
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
✓
Time-based split with last 30 days as validation
Time-based splitting preserves the chronological order of observations, which is essential for time-series forecasting because future values depend on past values. Using the most recent 30 days as the validation set simulates the real deployment scenario where the model predicts unseen future data. Random, stratified, or k-fold splits would mix past and future observations, causing data leakage and overly optimistic validation metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Stratified split based on sales volume
Why it's wrong here
Stratification preserves the class or value distribution across splits but ignores chronological order, so training rows can postdate validation rows and leak future sales into the model. It suits classification with imbalanced labels. Time-series forecasting needs a temporal cutoff, with the latest 30 days reserved as validation.
- ✗
K-fold cross-validation
Why it's wrong here
K-fold cross-validation shuffles observations across folds, so training folds contain days later than validation folds, leaking future information into the past. It suits independent, identically distributed samples. Time-series forecasting requires chronological ordering, with the most recent 30 days held out as validation.
- ✗
Random 80/20 split
Why it's wrong here
A random 80/20 split scatters days arbitrarily, placing later dates in training and earlier dates in validation, which leaks future information backwards. Random splitting suits independent observations. Time-series forecasting requires the validation set to be the chronologically most recent 30 days, with all earlier days used for training.
- ✓
Time-based split with last 30 days as validation
Why this is correct
Time-series data is ordered, so random splitting leaks future information into training. A time-based split trains on earlier observations and holds out the final 30 days, preserving chronological order and matching the requirement that validation be the most recent period.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.