Quick Answer
The answer is the log transformation (natural log). This is the correct choice because a log transformation compresses the upper end of a right-skewed distribution more than the lower end, pulling in long tails and making the data more symmetric. For right-skewed data, where the mean is greater than the median, applying a log function reduces the influence of extreme positive values, effectively normalizing the feature. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of feature engineering for model performance, often appearing in scenario-based questions about handling non-normal distributions. A common trap is confusing transformations: square or exponential transformations actually increase right skewness, while Min-Max scaling only rescales the range without altering shape. Remember the memory tip: “Logs shrink the long tail”—if your data trails off to the right, take the log to bring it back in line.
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 scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Log transformation (natural log)
Log transformation compresses high values, reducing right skew.
Min-Max scaling
Why wrong: Scaling does not change distribution shape.
One-hot encoding
Why wrong: One-hot encoding is for categorical variables.
Square transformation
Why wrong: Square transformation amplifies 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
Log transformation (natural log)
Option A is correct because log transformation is commonly used to reduce right skewness. Option B is wrong because square transformation increases skewness. Option C is wrong because Min-Max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical features.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Log transformation (natural log)
Why this is correct
Log transformation compresses high values, reducing right skew.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Min-Max scaling
Why it's wrong here
Scaling does not change distribution shape.
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
Square transformation
Why it's wrong here
Square transformation amplifies skewness.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Exploratory Data Analysis — study guide chapter
Learn the concepts, then practise the questions
Exploratory Data Analysis practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
The correct answer is: Log transformation (natural log) — Option A is correct because log transformation is commonly used to reduce right skewness. Option B is wrong because square transformation increases skewness. Option C is wrong because Min-Max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical features.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.