The correct choices are side-by-side boxplots and overlapping histograms with transparency. Side-by-side boxplots are ideal for visualizing numerical feature distribution across classes because they clearly display central tendency, spread, and potential outliers for each category in a compact, comparative format. Overlapping histograms with transparency allow you to directly compare the shape, modality, and skew of the distribution for each class without obscuring overlapping areas, making them equally effective for this binary target scenario. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match visualization techniques to data types and analytical goals—a common trap is confusing scatter plots (which require two numerical variables) or bar charts (which are for categorical counts) with distribution-focused plots. A useful memory tip: when you need to compare a single numerical feature across groups, think "box for outliers, hist for shape"—both reveal how the feature behaves per class.
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 data scientist is analyzing a dataset with a mix of numerical and categorical features. The target variable is binary. The data scientist wants to visualize the distribution of a numerical feature across the two target classes. Which TWO visualization techniques are appropriate? (Choose 2.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Overlapping histograms with transparency
Option A is correct because side-by-side boxplots show distribution and outliers across categories. Option C is correct because overlapping histograms with transparency show distribution shapes. Option B is wrong because scatter plots require two numerical variables. Option D is wrong because heatmaps are for correlation or contingency tables. Option E is wrong because bar charts are for categorical data, not numerical distributions.
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.
✗
Heatmap of the correlation matrix
Why it's wrong here
Heatmaps show correlations, not distributions.
✗
Stacked bar chart of the feature binned
Why it's wrong here
Bar charts are for categorical features.
✓
Overlapping histograms with transparency
Why this is correct
Histograms show distribution shapes; transparency allows comparison.
Related concept
Read the scenario before looking for a memorised answer.
✓
Side-by-side boxplots
Why this is correct
Boxplots compare distributions across categories.
Related concept
Read the scenario before looking for a memorised answer.
✗
Scatter plot with color-coded classes
Why it's wrong here
Scatter plots need two numerical variables.
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
Heatmaps show correlations, not distributions.
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: Overlapping histograms with transparency — Option A is correct because side-by-side boxplots show distribution and outliers across categories. Option C is correct because overlapping histograms with transparency show distribution shapes. Option B is wrong because scatter plots require two numerical variables. Option D is wrong because heatmaps are for correlation or contingency tables. Option E is wrong because bar charts are for categorical data, not numerical distributions.
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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