The answer is to plot the distribution of each feature separately for the positive and negative classes. This exploratory analysis step for class imbalance is critical because it directly reveals feature separability—if the distributions overlap heavily, the model will struggle to distinguish the rare positive class from the majority, regardless of sampling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that before any remediation (like SMOTE or undersampling), you must first diagnose whether the imbalance is due to a lack of signal or a true data skew. A common trap is jumping to correlation analysis or PCA, but those miss the core issue: feature overlap is the root cause of poor performance in imbalanced binary classification. Memory tip: think “Distributions First, Sampling Second”—always visualize how the classes separate along each feature axis before deciding on a resampling strategy.
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 company is preparing a dataset for training a binary classification model. The dataset has a severe class imbalance (1% positive class). The data scientist wants to understand the impact of this imbalance on model performance before sampling. Which exploratory analysis step is MOST critical?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Plot the distribution of each feature separately for the positive and negative classes.
Option B is correct because analyzing the distribution of features across classes can reveal separability and potential issues. Option A is wrong because correlation with target is not the primary concern. Option C is wrong because missing values are not the immediate concern. Option D is wrong because PCA is not necessary at this stage.
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.
✗
Compute the correlation matrix of all features with the target variable.
Why it's wrong here
Correlation may be low due to imbalance, not directly informative.
✗
Check for missing values and outliers in the dataset.
Why it's wrong here
Important but not the most critical for imbalance.
✗
Perform PCA and visualize the first two principal components colored by class.
Why it's wrong here
PCA may not show separation in imbalanced data.
✓
Plot the distribution of each feature separately for the positive and negative classes.
Why this is correct
Overlapping distributions indicate difficulty in classification.
Related concept
Read the scenario before looking for a memorised answer.
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.
Trap categories for this question
Command / output trap
PCA may not show separation in imbalanced data.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Plot the distribution of each feature separately for the positive and negative classes. — Option B is correct because analyzing the distribution of features across classes can reveal separability and potential issues. Option A is wrong because correlation with target is not the primary concern. Option C is wrong because missing values are not the immediate concern. Option D is wrong because PCA is not necessary at this stage.
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.
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Question Discussion
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