MLS-C01 Exploratory Data Analysis Practice Question
A data scientist uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?
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
✓
Scree plot of principal components
A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a histogram shows distribution, not variance. Option C is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option D is wrong because a box plot shows summary statistics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Histogram of the target variable
Why it's wrong here
Histogram shows target distribution, not feature variance.
- ✓
Scree plot of principal components
Why this is correct
Scree plot displays variance explained by each component.
- ✗
Heatmap of feature correlations
Why it's wrong here
Heatmap shows correlations, not variance explained.
- ✗
Box plot of each feature
Why it's wrong here
Box plots show quartiles and outliers, not variance explained.
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