The correct EDA step to prioritize is analyzing the distribution of each feature separately for churn and non-churn groups. This approach directly addresses the imbalance by revealing how individual features behave within each class, allowing the team to identify which variables are most predictive of the minority churn class. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding that EDA for imbalanced datasets must go beyond global statistics—a common trap is to apply dimensionality reduction like PCA or jump to modeling steps such as cross-validation, which do not diagnose class-specific feature patterns. A reliable memory tip is to think “split before you model”: always separate your data by target class during EDA to uncover hidden drivers of the minority group.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 machine learning team is building a model to predict customer churn. The dataset has 20 features and 50,000 rows. After initial EDA, they notice that the target variable 'churn' is highly imbalanced (5% churn, 95% non-churn). Which EDA step should the team prioritize to address this imbalance before model training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Analyze the distribution of each feature separately for churn and non-churn groups.
Option D is correct because understanding the distribution of features across churn and non-churn classes helps identify which features drive churn. Option A is wrong because PCA reduces dimensionality but does not address imbalance. Option B is wrong because cross-validation is a modeling step, not EDA. Option C is wrong because removing outliers may worsen imbalance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Remove outliers in the majority class to balance the dataset.
Why it's wrong here
Removing data may discard useful information and reduce dataset size.
✓
Analyze the distribution of each feature separately for churn and non-churn groups.
Why this is correct
This helps identify which features differentiate the classes and informs whether resampling or cost-sensitive methods are needed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Perform stratified cross-validation to ensure balanced folds.
Why it's wrong here
Cross-validation is a modeling technique, not an EDA step.
✗
Apply Principal Component Analysis (PCA) to reduce noise.
Why it's wrong here
PCA does not address class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Analyze the distribution of each feature separately for churn and non-churn groups. — Option D is correct because understanding the distribution of features across churn and non-churn classes helps identify which features drive churn. Option A is wrong because PCA reduces dimensionality but does not address imbalance. Option B is wrong because cross-validation is a modeling step, not EDA. Option C is wrong because removing outliers may worsen imbalance.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is exploring a dataset with 100 features. The goal is to build a binary classification model. The dataset is highly imbalanced with 95% negative class and 5% positive class. The data scientist wants to understand the relationship between features and the target. Which technique is most appropriate for initial exploratory analysis?
easy
A.Remove the minority class samples and analyze the majority class only.
✓ B.Use stratified sampling to create a balanced subset for visualization and correlation analysis.
C.Use random sampling to select 10% of the data for EDA.
D.Apply SMOTE to the dataset before performing EDA.
Why B: Option A is correct because stratified sampling ensures that the minority class is adequately represented in the sample. Option B is wrong because random sampling may miss the rare class entirely. Option C is wrong because SMOTE is a data augmentation technique for training, not for EDA. Option D is wrong because removing the minority class would prevent analyzing the target.
Variation 2. A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?
medium
A.Run a t-test for each feature to determine statistical significance between classes.
B.Generate box plots for each feature using Amazon QuickSight.
✓ C.Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.
D.Compute the correlation matrix between features and the target.
Why C: Using Amazon SageMaker Data Wrangler to generate histograms segmented by class is a straightforward way to visualize feature distributions for each class. Option B (t-tests) may be used later but doesn't provide distribution visualization. Option C (box plots) is a good alternative but not as comprehensive as histograms for distribution shape. Option D (correlation matrix) does not show class-wise distribution.
Variation 3. A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?
hard
A.Normalize all numerical features to have zero mean and unit variance.
B.Remove outliers from the dataset using the IQR method.
✓ C.Create a stratified train-test split to preserve the class distribution.
D.Perform correlation analysis to remove highly correlated features.
Why C: Stratified sampling ensures the rare class is represented in train/test splits, preserving the imbalanced ratio for evaluation. Option A is wrong because normalization does not address imbalance. Option C is wrong because correlation analysis is not specific to imbalance. Option D is wrong because removing outliers could eliminate fraud cases.
Last reviewed: Jun 20, 2026
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This MLS-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 MLS-C01 exam.
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