MLA-C01 Data Preparation for Machine Learning Practice Question

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

⚠ Common exam trap

AWS often tests the concept of data leakage by presenting random or stratified splits as viable options, trapping candidates who overlook the time-based column and assume standard splitting methods are always safe.

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

✓

Temporal split based on date

A temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

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 target
Why it's wrong here
Stratification maintains class proportions but ignores time order.
✓
Temporal split based on date
Why this is correct
Temporal split respects chronology by using earlier data for training and later data for testing.
✗
Random split with 70/20/10
Why it's wrong here
Random split disregards time order and can leak future information into training.
✗
K-fold cross-validation
Why it's wrong here
Standard k-fold is random and can cause leakage in time series.

Go deeper

MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

Related to this question