MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist discovers that a numerical feature 'income' has a skewness of 3.5. Which transformation should the scientist apply to make the distribution more symmetric?
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
A log transformation is commonly applied to right-skewed positive data to reduce skewness and make the distribution more symmetric. Option A is wrong because standardization (Z-score) centers and scales the data but does not change the shape of the distribution. Option B is wrong because a square transformation would increase skewness for right-skewed data. Option D is wrong because min-max scaling rescales the data to a fixed range but does not alter the distribution's skewness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardization (Z-score)
Why it's wrong here
Standardization centers and scales but does not reduce skewness.
- ✗
Square transformation
Why it's wrong here
Square transformation would make the skewness worse for right-skewed data.
- ✓
Log transformation
Why this is correct
Log transformation compresses the tail and reduces right skewness.
- ✗
Min-Max scaling
Why it's wrong here
Min-Max scaling does not alter the shape of the distribution.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.