Quick Answer
The correct answer is to apply a log transformation, specifically log(Price). This is the standard technique for right-skewed data because it compresses the long tail of extreme high values, pulling them closer to the center and making the distribution more symmetric and approximately normal. The logarithm is a monotonic, concave function that reduces multiplicative differences into additive ones, which directly addresses the skewness that violates the normality assumption required by linear models. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering for model assumptions, often appearing in the Data Engineering domain. A common trap is confusing scaling (like min-max) with transformation—scaling changes the range but not the shape, while log transformation changes the shape. Remember the mnemonic: "Right-skewed? Log it to level it."
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 working with a dataset that contains a 'Price' column. After plotting a histogram, they observe that the distribution is right-skewed with many extreme high values. They plan to use a linear model that assumes normally distributed errors. Which of the following transformations should they apply to the 'Price' column to make it more normally distributed?
Apply log transformation (log(Price)).
Log transformation compresses the tail and makes the distribution more symmetric.
Apply square transformation (Price^2).
Why wrong: Square transformation makes right skew worse.
Apply min-max scaling to the 'Price' column.
Why wrong: Scaling does not change the shape of the distribution.
Bin the 'Price' values into equal-width intervals.
Why wrong: Binning discretizes the data and loses information.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Apply log transformation (log(Price)).
Option D is correct because log transformation is commonly used for right-skewed data to reduce skewness. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because square transformation increases skewness. Option C is wrong because binning loses information.
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.
Apply log transformation (log(Price)).
Why this is correct
Log transformation compresses the tail and makes the distribution more symmetric.
Related concept
Read the scenario before looking for a memorised answer.
Apply square transformation (Price^2).
Why it's wrong here
Square transformation makes right skew worse.
Apply min-max scaling to the 'Price' column.
Why it's wrong here
Scaling does not change the shape of the distribution.
Bin the 'Price' values into equal-width intervals.
Why it's wrong here
Binning discretizes the data and loses information.
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: Apply log transformation (log(Price)). — Option D is correct because log transformation is commonly used for right-skewed data to reduce skewness. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because square transformation increases skewness. Option C is wrong because binning loses information.
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.
About these practice questions
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?
mediumWhy B: Log transformation compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.
Variation 2. During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?
easyWhy A: A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.
Last reviewed: Jun 20, 2026
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