The answer is a bar chart of class frequencies combined with a correlation heatmap. A bar chart directly visualizes class imbalance by showing the stark count difference between the 99% negative and 1% positive classes, making the severity of the imbalance immediately apparent, while a correlation heatmap reveals how each feature relates to the binary target, guiding which variables might inform a resampling strategy like SMOTE or undersampling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between EDA techniques and model evaluation tools—a common trap is confusing a confusion matrix (used post-modeling) with initial exploration. Remember that for a binary target, scatterplot matrices are for continuous variables only, and box plots show feature distributions per class but not the imbalance itself. Memory tip: “Count the bars, then map the correlations” to lock in the correct pair for visualizing imbalance before modeling.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Bar chart of class frequencies and a correlation heatmap
Option D is correct because a bar chart of class counts clearly shows the imbalance, and a correlation heatmap helps understand feature relationships with the target. Option A is wrong because a scatterplot matrix is for continuous variables, not for a binary target. Option B is wrong because box plots show distribution of continuous features by class, but not the imbalance itself. Option C is wrong because a confusion matrix is for model evaluation, not for initial data exploration.
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.
✗
Confusion matrix on a sample of the data
Why it's wrong here
Confusion matrix is used after model predictions, not for initial EDA.
✗
Scatterplot matrix of all features colored by class
Why it's wrong here
Scatterplot matrix is useful for continuous variables but does not directly show class imbalance.
✗
Box plots of each feature grouped by the target class
Why it's wrong here
Box plots show distribution differences but not the overall imbalance ratio.
✓
Bar chart of class frequencies and a correlation heatmap
Why this is correct
Bar chart shows imbalance clearly; correlation heatmap helps identify features related to the target.
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
Similar concept trap
Confusion matrix is used after model predictions, not for initial EDA.
Command / output trap
Scatterplot matrix is useful for continuous variables but does not directly show class imbalance.
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: Bar chart of class frequencies and a correlation heatmap — Option D is correct because a bar chart of class counts clearly shows the imbalance, and a correlation heatmap helps understand feature relationships with the target. Option A is wrong because a scatterplot matrix is for continuous variables, not for a binary target. Option B is wrong because box plots show distribution of continuous features by class, but not the imbalance itself. Option C is wrong because a confusion matrix is for model evaluation, not for initial data exploration.
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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