The answer is to visualize the distribution of numerical features using histograms and box plots. This is a core best practice because histograms reveal the shape, skewness, and modality of a numerical feature’s distribution, while box plots highlight outliers, quartiles, and spread—both essential for understanding data structure before modeling. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to perform effective EDA best practices for numerical and categorical features, often appearing in scenario-based questions where you must choose the most appropriate visualization or preprocessing step. A common trap is to focus solely on summary statistics like mean and variance, which can mask outliers or multimodal distributions that visualizations would immediately expose. Remember the memory tip: “Box for spread, hist for shape” to quickly recall that box plots handle spread and outliers, while histograms handle distribution shape.
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.
Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the proportion of missing values for each feature.
Option A is correct because checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.
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.
✓
Check the proportion of missing values for each feature.
Why this is correct
Missing value analysis is a key EDA step.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✓
Compute pairwise correlation coefficients between numerical features.
Why this is correct
Correlation analysis detects multicollinearity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Encode all categorical features using label encoding for simplicity.
Why it's wrong here
Label encoding can introduce false ordinal relationships.
✗
Include all categorical features with high cardinality as-is in the model.
Why it's wrong here
High cardinality features often need encoding or grouping.
✓
Visualize the distribution of numerical features using histograms and box plots.
Why this is correct
Visualization helps identify skewness and outliers.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume label encoding is harmless for categorical features, but it imposes an artificial order that can distort model behavior, especially in tree-based models that rely on split points.
Detailed technical explanation
How to think about this question
Pairwise correlation coefficients (Option B) quantify linear relationships between numerical features, using Pearson's r, which ranges from -1 to 1. This helps detect multicollinearity, which can destabilize models like linear regression by inflating coefficient variances. Visualizing distributions (Option E) with histograms and box plots reveals skewness, outliers, and modality, guiding transformations like log scaling or winsorization to meet model assumptions.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Check the proportion of missing values for each feature. — Option A is correct because checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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