Quick Answer
The answer is log transformation, the most appropriate technique for handling right-skewed distributions like income. This works because log transformation compresses the long tail of high values while expanding the lower end, effectively pulling the distribution toward symmetry—ideal for features that follow a log-normal pattern, which income data often does. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing for model performance, often appearing in exploratory data analysis scenarios where you must choose between transformations like log, square root, or Box-Cox. A common trap is selecting square root for moderate skewness, but log is preferred for heavy right skewness because its compression effect is stronger. Remember the memory tip: “Log for the long tail”—when the tail stretches far right, log transformation brings it back into sight.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
In exploratory data analysis, a data scientist notices that the distribution of a feature 'income' is heavily right-skewed. Which transformation is most appropriate to reduce skewness?
Standardization (z-score).
Why wrong: Standardization does not change shape.
Square transformation.
Why wrong: Square can increase skewness for right-skewed data.
Min-max scaling.
Why wrong: Scaling does not affect skewness.
Log transformation.
Log transformation reduces right skew.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Log transformation.
Log transformation is the most appropriate technique to reduce right skewness in a feature like 'income' because it compresses the long tail of high values while expanding the lower end, making the distribution more symmetric. This is particularly effective for income data, which often follows a log-normal distribution, and is a standard preprocessing step in machine learning to improve model performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 does not change shape.
Square transformation.
Why it's wrong here
Square can increase skewness for right-skewed data.
Min-max scaling.
Why it's wrong here
Scaling does not affect skewness.
Log transformation.
Why this is correct
Log transformation reduces right skew.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse scaling techniques (which change range or variance) with transformations that alter distribution shape, leading them to pick standardization or min-max scaling as a fix for skewness.
Detailed technical explanation
Log transformation applies the natural logarithm (or base 10) to each value, which is a monotonic transformation that effectively reduces the impact of outliers and heteroscedasticity. In practice, a small constant (e.g., 1) is often added to avoid log(0) when the feature contains zero values. This transformation is particularly common in financial and economic datasets where features like income, house prices, or transaction amounts exhibit multiplicative growth patterns.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Log transformation. — Log transformation is the most appropriate technique to reduce right skewness in a feature like 'income' because it compresses the long tail of high values while expanding the lower end, making the distribution more symmetric. This is particularly effective for income data, which often follows a log-normal distribution, and is a standard preprocessing step in machine learning to improve model performance.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. During exploratory data analysis, a data scientist notices that the distribution of a continuous feature is heavily right-skewed. Which transformation should be applied to make the distribution more symmetric for linear regression?
mediumWhy D: Log transformation is commonly used for right-skewed data. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because standardization does not fix skewness. Option D is wrong because one-hot encoding is for categorical features.
Variation 2. During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?
easyWhy A: Option C is correct because log transformation is commonly used to reduce right skewness. Option A is wrong because standardization does not change distribution shape. Option B is wrong because min-max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical variables, not continuous.
Last reviewed: Jun 24, 2026
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