hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A financial services company needs to build a…
A financial services company needs to build a fraud detection model using historical transaction data. The dataset has a timestamp column, and the model must be evaluated on its ability to detect fraud in future unseen transactions. The data is imbalanced (fraud is rare). Which TWO data splitting strategies should the engineer use for model validation? (Select TWO.)
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
✓
Stratified k-fold cross-validation
Time-series split (walk-forward validation) respects temporal order, and stratified k-fold cross-validation maintains class proportions across folds. Simple random split ignores time; k-fold without stratification may produce folds without fraud.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
k-fold cross-validation without stratification
Why it's wrong here
May result in folds with no fraud cases, making evaluation unreliable.
- ✗
Leave-one-out cross-validation
Why it's wrong here
Computationally expensive and not appropriate for time-series or imbalanced data.
- ✓
Stratified k-fold cross-validation
Why this is correct
Maintains class proportions in each fold, important for imbalanced data.
- ✗
Simple random split
Why it's wrong here
May leak future information into training and does not preserve temporal order.
- ✓
Time-series split (walk-forward validation)
Why this is correct
Preserves temporal order, training on past and validating on future.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.