This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?
K-fold cross-validation split
Why wrong: K-fold is a cross-validation technique, not a single train/validation/test split.
Chronological split
Why wrong: Chronological split is for time-series data, not categorical targets.
Stratified split
Stratified split ensures each subset has the same class distribution as the original dataset.
Random split
Why wrong: Random split may not maintain class proportions across splits.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Stratified split
Option C is correct because stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
K-fold cross-validation split
Why it's wrong here
K-fold is a cross-validation technique, not a single train/validation/test split.
Chronological split
Why it's wrong here
Chronological split is for time-series data, not categorical targets.
Stratified split
Why this is correct
Stratified split ensures each subset has the same class distribution as the original dataset.
Related concept
Read the scenario before looking for a memorised answer.
Random split
Why it's wrong here
Random split may not maintain class proportions across splits.
Common exam traps
AWS often tests the distinction between data splitting techniques and model evaluation methods, so the trap here is that candidates confuse k-fold cross-validation (a validation strategy) with a static split technique, leading them to select option A.
Detailed technical explanation
Stratified splitting works by partitioning the dataset based on the target variable's strata, then applying random sampling within each stratum to maintain the original class ratio. In scikit-learn, `train_test_split` with `stratify=y` implements this by using the target array to ensure each split has the same percentage of samples for each class as the full dataset. This is especially important in medical diagnosis or fraud detection where the minority class may be less than 1% of the data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — study guide chapter
Learn the concepts, then practise the questions
Data Preparation for Machine Learning practice questions
Targeted practice on this topic area only
All MLA-C01 questions
507 questions across all exam domains
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Practise MLA-C01 questions linked to ML Model Development.
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
Practise MLA-C01 questions linked to MLA-C01 scenario.
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Stratified split — Option C is correct because stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
Last reviewed: Jun 30, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.