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MLA-C01 Practice Question: Which feature scaling method is most robust to…
Which feature scaling method is most robust to outliers in the data?
⚠ Common exam trap
AWS often tests the misconception that Standardization (Z-score) is robust because it uses standard deviation, but candidates forget that both mean and standard deviation are outlier-sensitive, whereas robust scaling explicitly uses median and IQR.
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
Robust scaling is the most robust to outliers because it uses the median and interquartile range (IQR) instead of the mean and standard deviation. The median and IQR are not influenced by extreme values, so the scaled features remain stable even when the data contains significant outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalization (L2)
Why it's wrong here
Normalization scales by vector magnitude, not robust to outliers in each feature.
- ✗
Standardization (Z-score)
Why it's wrong here
Standardization uses mean and standard deviation, which are influenced by outliers.
- ✓
Robust scaling
Why this is correct
Robust scaling centres data using the median and divides by the interquartile range, so extreme values do not distort the transformation. Standardisation and min-max scaling both rely on the mean and range, which outliers skew heavily, making robust scaling the appropriate choice here.
- ✗
Min-max scaling
Why it's wrong here
Min-max scaling is highly affected by outliers as it depends on min and max values.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.