MLS-C01 Exploratory Data Analysis Practice Question
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.
During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply a log transformation to the feature.
A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Apply a log transformation to the feature.
Why this is correct
Log transformation compresses high values and can make the distribution more symmetric.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply z-score normalization.
Why it's wrong here
Z-score normalization centers and scales but does not change skewness.
✗
Apply one-hot encoding.
Why it's wrong here
One-hot encoding is for categorical variables, not for transforming numerical features.
✗
Apply min-max scaling.
Why it's wrong here
Min-max scaling does not affect the shape of the distribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between scaling (which changes range) and transformation (which changes distribution shape), so the trap here is that candidates might pick min-max scaling or z-score normalization thinking they handle outliers, but they only rescale without fixing skewness.
Detailed technical explanation
How to think about this question
Log transformation is a variance-stabilizing transformation that maps multiplicative relationships to additive ones, making it particularly effective for features like income, population, or latency that span several orders of magnitude. A subtle behavior is that log(0) is undefined, so a common practice is to add a small constant (e.g., log(x+1)) if the feature contains zeros. In real-world scenarios, such as modeling website session durations or financial transaction amounts, log transformation often enables linear regression to meet normality assumptions.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply a log transformation to the feature. — A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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