MLS-C01 Exploratory Data Analysis Practice Question
During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?
⚠ Common exam trap
AWS often tests the distinction between scaling (which changes range) and transformation (which changes distribution shape), so the trap here is that candidates might pick min-max scaling or z-score normalization thinking they handle outliers, but they only rescale without fixing skewness.
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
✓
Apply a log transformation to the feature.
A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.
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 a log transformation to the feature.
Why this is correct
Log transformation compresses high values and can make the distribution more symmetric.
- ✗
Apply z-score normalization.
Why it's wrong here
Z-score normalization centers and scales but does not change skewness.
- ✗
Apply one-hot encoding.
Why it's wrong here
One-hot encoding is for categorical variables, not for transforming numerical features.
- ✗
Apply min-max scaling.
Why it's wrong here
Min-max scaling does not affect the shape of the distribution.
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