MLA-C01 Data Preparation for Machine Learning Practice Question
A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?
⚠ Common exam trap
Many candidates confuse scaling (standardization or min-max) with shape-changing transformations, assuming any normalization makes data Gaussian, when in fact only non-linear transformations like log or Box-Cox address 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
✓
Log transformation (natural log)
A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Log transformation (natural log)
Why this is correct
A natural log transformation compresses the long right tail of a heavily skewed variable, pulling extreme high values closer to the bulk of the data. This reduces skewness and stabilises variance, making the 'income' distribution approximately Gaussian-like, which many ML algorithms assume.
- ✗
Standardization (z-score)
Why it's wrong here
Centers and scales but does not change distribution shape.
- ✗
Min-max scaling to [0,1]
Why it's wrong here
Min-max scaling rescales values linearly into [0,1] without altering the distribution's shape, so right skew persists. It is correct when features need bounded ranges for distance-based algorithms, not when the goal is a Gaussian-like distribution.
- ✗
Equal-width binning
Why it's wrong here
Equal-width binning discretises continuous values into fixed ranges, producing a categorical or ordinal feature rather than reshaping the numeric distribution toward Gaussian. It is tempting because binning handles outliers and non-linearity for linear models, and would suit converting skewed numeric input into bands for tree-based models.
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