Question 672 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist examines a dataset with 100 features and suspects that some features are redundant due to high pairwise correlations. Which EDA technique should the scientist use to systematically identify groups of highly correlated features?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Generate a correlation matrix and visualize it as a heatmap.
A correlation matrix heatmap allows systematic identification of groups of highly correlated features by visually highlighting high pairwise correlations. Option B is incorrect because histograms show univariate distributions, not relationships between features. Option C is incorrect because scatter plots for each pair would be time-consuming and not systematic for 100 features. Option D is incorrect because box plots show outliers, not correlations.
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 correlation matrix and visualize it as a heatmap.
Why this is correct
Heatmap of correlation matrix quickly reveals high pairwise correlations.
- ✗
Plot histograms for each feature.
Why it's wrong here
Histograms show distribution, not correlation between features.
- ✗
Create scatter plots for each pair of features.
Why it's wrong here
Scatter plots are useful but not systematic for 100 features (too many pairs).
- ✗
Use box plots to identify outliers.
Why it's wrong here
Box plots show outliers, not correlations.
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Last reviewed: Jun 20, 2026
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