MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is exploring a dataset with a target variable that is highly imbalanced. The minority class represents only 1% of the data. Which technique should the analyst use to better understand the relationships between features and the minority class?
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
✓
Use stratified sampling to create a balanced sample for analysis.
Stratified sampling ensures the minority class is proportionally represented in the sample, allowing meaningful analysis. Option A is wrong because SMOTE generates synthetic data, which is not appropriate for initial exploratory analysis. Option B is wrong because random sampling may miss the minority class entirely. Option C is wrong because scaling features does not address class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply SMOTE to the dataset before analysis.
Why it's wrong here
SMOTE is for modeling, not exploratory analysis.
- ✗
Use random sampling to reduce the dataset size.
Why it's wrong here
Random sampling may underrepresent the minority class.
- ✗
Scale the features using Min-Max scaling.
Why it's wrong here
Feature scaling does not address imbalance.
- ✓
Use stratified sampling to create a balanced sample for analysis.
Why this is correct
Stratified sampling preserves class proportions.
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