Quick Answer
The correct interpretation is that a skewness value of 2.5 indicates the distribution is right-skewed, also known as positively skewed. This is because skewness measures the asymmetry of a distribution: a value greater than 1 is considered highly right-skewed, meaning the tail on the right side is longer or fatter than the left, pulling the mean to the right of the median. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to interpret skewness value during exploratory data analysis, often appearing in questions about feature engineering or model assumptions. A common trap is confusing positive skew with left skew—remember that positive numbers point to the right tail. A useful memory tip is to think of the skewness sign as pointing toward the direction of the tail: positive skew means the tail points right, like a positive arrow.
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.
During EDA, a data scientist finds that a feature has a skewness value of 2.5. What does this indicate about the data distribution?
The distribution is right-skewed
Positive skewness indicates a long right tail.
The distribution is symmetric
Why wrong: Symmetric distributions have skewness near 0.
The distribution is left-skewed
Why wrong: Left-skewed has negative skewness.
The distribution has no outliers
Why wrong: Skewness does not directly measure outliers.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The distribution is right-skewed
A skewness > 1 indicates a highly right-skewed (positive skew) distribution, meaning the tail extends to the right. Option A is wrong because left skew is negative. Option B is wrong because symmetric distributions have skewness near 0. Option D is wrong because skewness describes shape, not the presence of outliers specifically.
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.
The distribution is right-skewed
Why this is correct
Positive skewness indicates a long right tail.
Related concept
Read the scenario before looking for a memorised answer.
The distribution is symmetric
Why it's wrong here
Symmetric distributions have skewness near 0.
The distribution is left-skewed
Why it's wrong here
Left-skewed has negative skewness.
The distribution has no outliers
Why it's wrong here
Skewness does not directly measure outliers.
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: The distribution is right-skewed — A skewness > 1 indicates a highly right-skewed (positive skew) distribution, meaning the tail extends to the right. Option A is wrong because left skew is negative. Option B is wrong because symmetric distributions have skewness near 0. Option D is wrong because skewness describes shape, not the presence of outliers specifically.
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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