Quick Answer
The correct answer is the logarithmic transformation, which is the most effective method to reduce right skewness with log transformation because it compresses the long tail of the distribution by applying a concave function to large values, pulling them closer to the center while preserving the order of the data. This works because right skewness is characterized by a few extremely high values, and the log function dampens their influence exponentially, making the histogram more symmetrical. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing for feature engineering, often appearing in questions about handling non-normal distributions before applying linear models or PCA. A common trap is choosing standardization, which only rescales the data without altering its shape, or the square root transformation, which is weaker for severe skewness. Remember the mnemonic: “Log for the long tail” — when you see a histogram stretching far to the right, think log first.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A data analyst is examining the distribution of a continuous variable and notices that its histogram is heavily skewed to the right. Which transformation should the analyst apply to make the distribution more symmetrical?
Box-Cox transformation with lambda=2.
Why wrong: Box-Cox with lambda=2 would increase skewness.
Logarithmic transformation (log).
Log transformation reduces right skewness.
Standardization (z-score).
Why wrong: Standardization does not change distribution shape.
Square root transformation.
Why wrong: Square root is less effective for right skewness.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Logarithmic transformation (log).
Option B is correct because log transformation is commonly used to reduce right skewness by compressing the long tail. Option A is wrong because the square root transformation is less effective for severe skewness. Option C is wrong because Box-Cox requires all values positive and is a family that includes log, but the log is a specific case. Option D is wrong because standardization does not change the shape of the distribution.
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.
Box-Cox transformation with lambda=2.
Why it's wrong here
Box-Cox with lambda=2 would increase skewness.
Logarithmic transformation (log).
Why this is correct
Log transformation reduces right skewness.
Related concept
Read the scenario before looking for a memorised answer.
Standardization (z-score).
Why it's wrong here
Standardization does not change distribution shape.
Square root transformation.
Why it's wrong here
Square root is less effective for right skewness.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Logarithmic transformation (log). — Option B is correct because log transformation is commonly used to reduce right skewness by compressing the long tail. Option A is wrong because the square root transformation is less effective for severe skewness. Option C is wrong because Box-Cox requires all values positive and is a family that includes log, but the log is a specific case. Option D is wrong because standardization does not change the shape of the distribution.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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