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
Reduces right skewness, makes distribution more symmetric.
✗
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
Only rescales, does not affect skewness.
✗
Equal-width binning
Why it's wrong here
Converts continuous to categorical, discards information.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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