The answer is scatter plots for numerical features and box plots for categorical features. These are the standard EDA techniques for a continuous target variable because they directly reveal the form and strength of relationships: scatter plots show correlation, trend, and outliers between a numerical predictor and the continuous target, while box plots compare the target’s distribution across categories of a categorical feature, highlighting differences in median, spread, and skew. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match visualization type to variable type and task—a common trap is confusing classification metrics (like confusion matrices) or univariate plots (like histograms) with bivariate EDA for regression. Remember the pairing rule: numerical vs. continuous target = scatter plot; categorical vs. continuous target = box plot. A quick memory tip: “Scatter for numbers, box for buckets.”
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 data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create scatter plots of numerical features against the target variable.
Scatter plots for numerical vs continuous target and box plots for categorical vs continuous target are standard. Option C (confusion matrix) is for classification. Option D (histogram of target) is univariate. Option E (silhouette score) is for clustering.
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.
✗
Generate a confusion matrix for the target variable.
Why it's wrong here
Confusion matrix is for classification evaluation.
✗
Compute the silhouette score for each feature.
Why it's wrong here
Silhouette score is for clustering validation.
✓
Create scatter plots of numerical features against the target variable.
Why this is correct
Reveals linear/nonlinear relationships.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use box plots to compare target distribution across categorical feature categories.
Why this is correct
Shows differences in central tendency and spread.
Related concept
Read the scenario before looking for a memorised answer.
✗
Plot a histogram of the target variable.
Why it's wrong here
Univariate, does not show relationships with features.
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 for classification evaluation.
Command / output trap
Univariate, does not show relationships with features.
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: Create scatter plots of numerical features against the target variable. — Scatter plots for numerical vs continuous target and box plots for categorical vs continuous target are standard. Option C (confusion matrix) is for classification. Option D (histogram of target) is univariate. Option E (silhouette score) is for clustering.
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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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.
Question Discussion
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