hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is building a…
A machine learning engineer is building a time-series forecasting model to predict daily sales for the next 30 days. The dataset spans two years of daily sales data. To evaluate model performance, the engineer needs to simulate a realistic forecasting scenario where the model is trained on past data and tested on future data without leakage. Which data splitting strategy should they use?
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
✓
Walk-forward validation with an expanding window
Walk-forward validation (also known as time-series cross-validation) is specifically designed for time-dependent data. It trains on an expanding window of historical data and tests on the next period, respecting temporal order.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hold-out validation using a random 80/20 split
Why it's wrong here
Random splitting can introduce future data into the training set, leading to optimistic performance estimates.
- ✓
Walk-forward validation with an expanding window
Why this is correct
Walk-forward validation trains on all past data and evaluates on the next unobserved time step, mimicking real-world forecasting.
- ✗
Stratified sampling based on sales volume
Why it's wrong here
Stratified sampling maintains class proportions but does not preserve time order.
- ✗
k-fold cross-validation with random shuffling
Why it's wrong here
Random shuffling destroys temporal order, causing data leakage from the future into the training set.
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