MLA-C01 Data Preparation for Machine Learning Practice Question

A dataset contains a numerical feature with extreme outliers. The outliers are genuine (not errors), and the ML model is a linear regression which is sensitive to outliers. Which data transformation should be applied to reduce the impact of outliers while preserving the data?

⚠ Common exam trap

AWS often tests the distinction between scaling methods that are robust to outliers versus those that are not, trapping candidates who assume all normalization techniques handle outliers equally.

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

✓

Robust scaling (median and IQR)

Robust scaling uses the median and interquartile range (IQR) to center and scale the data, making it resistant to extreme outliers. Since linear regression is sensitive to outliers, this transformation reduces their influence while preserving the original data distribution, unlike methods that rely on mean and variance.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Min-max scaling
Why it's wrong here
Min-max scaling is affected by min and max values, which can be outliers.
✗
Log transformation
Why it's wrong here
Log transformation reduces skew but does not eliminate outlier influence on scaling.
✓
Robust scaling (median and IQR)
Why this is correct
Robust scaling uses median and interquartile range, not affected by extreme values.
✗
Standardization (z-score)
Why it's wrong here
Standardization uses mean and standard deviation, both influenced by outliers.

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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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