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 plots the distribution of a numeric feature and observes that it is right-skewed. The feature will be used as input to a linear model. Which transformation should the data scientist apply?
Square transformation
Why wrong: Square transformation increases right skewness.
Log transformation
Log transformation compresses the tail and reduces right skewness.
One-hot encoding
Why wrong: One-hot encoding is for categorical features, not numeric.
Standardization (Z-score)
Why wrong: Standardization centers and scales but does not change 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
A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear 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.
Square transformation
Why it's wrong here
Square transformation increases right skewness.
Log transformation
Why this is correct
Log transformation compresses the tail and reduces right skewness.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical features, not numeric.
Standardization (Z-score)
Why it's wrong here
Standardization centers and scales but does not change skewness.
Common exam traps
Cisco often tests the misconception that standardization or scaling fixes skewness, but candidates must remember that only shape-altering transformations like log or Box-Cox address non-normality, not just rescaling.
Detailed technical explanation
The log transformation is a variance-stabilizing transformation that maps multiplicative relationships to additive ones, which is particularly effective for right-skewed data where the variance increases with the mean. Under the hood, applying log(x+1) is common when the feature contains zero values to avoid undefined results. In real-world scenarios like predicting house prices, log-transforming the target or skewed features like 'square footage' often yields better linear model fit and interpretability.
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 — A right-skewed distribution indicates that the feature has a long tail on the right, which can violate the linear model assumption of normally distributed errors. The log transformation compresses the high values and expands the low values, making the distribution more symmetric and stabilizing variance, which improves linear 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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Last reviewed: Jun 24, 2026
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