- A
Remove all rows with outlier values.
Why wrong: Incorrect. Removing all rows with outliers can discard valuable information and introduce bias, especially if outliers are natural variations.
- B
Apply a logarithmic transformation to the feature.
Why wrong: Incorrect. Log transformation reduces skewness but does not eliminate the influence of extreme outliers; they remain as high or low values on the log scale.
- C
Standardize the feature using Z-score normalization.
Why wrong: Incorrect. Z-score normalization rescales data but does not reduce the magnitude of outliers relative to the rest of the data, so linear regression is still affected.
- D
Cap the feature values at the 1st and 99th percentiles.
Correct. Capping at percentiles limits extreme values, reducing their impact while preserving data size.
Handle Outliers with Winsorizing (Capping) for Linear Models
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. A key principle to apply: winsorization. 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 numeric feature has many outliers. The feature will be used in a linear regression model. Which approach should the scientist take to handle the outliers?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cap the feature values at the 1st and 99th percentiles.
Option D is correct because capping (winsorizing) the feature values at the 1st and 99th percentiles limits the influence of extreme outliers while retaining all data points. This is particularly important for linear regression, which is sensitive to outliers. Option A is wrong because removing all rows with outliers can lead to significant data loss and bias. Option B is wrong because a logarithmic transformation reduces skew but does not eliminate the impact of outliers; it only compresses their range. Option C is wrong because Z-score normalization standardizes the data but does not reduce the influence of outliers; extreme values remain extreme relative to the distribution.
Key principle: Winsorization
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all rows with outlier values.
Why it's wrong here
Incorrect. Removing all rows with outliers can discard valuable information and introduce bias, especially if outliers are natural variations.
- ✗
Apply a logarithmic transformation to the feature.
Why it's wrong here
Incorrect. Log transformation reduces skewness but does not eliminate the influence of extreme outliers; they remain as high or low values on the log scale.
- ✗
Standardize the feature using Z-score normalization.
Why it's wrong here
Incorrect. Z-score normalization rescales data but does not reduce the magnitude of outliers relative to the rest of the data, so linear regression is still affected.
- ✓
Cap the feature values at the 1st and 99th percentiles.
Why this is correct
Correct. Capping at percentiles limits extreme values, reducing their impact while preserving data size.
Related concept
Winsorization
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse capping (winsorization) with standardization or transformation. Standardization does not mitigate outliers; it only rescales the data. The key is to limit extreme values using percentile-based capping.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Winsorization
- Outlier handling for linear regression
- Log transformation
- Z-score normalization
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
Winsorization
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. Winsorization 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.
Review winsorization, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Winsorization.
What is the correct answer to this question?
The correct answer is: Cap the feature values at the 1st and 99th percentiles. — Option D is correct because capping (winsorizing) the feature values at the 1st and 99th percentiles limits the influence of extreme outliers while retaining all data points. This is particularly important for linear regression, which is sensitive to outliers. Option A is wrong because removing all rows with outliers can lead to significant data loss and bias. Option B is wrong because a logarithmic transformation reduces skew but does not eliminate the impact of outliers; it only compresses their range. Option C is wrong because Z-score normalization standardizes the data but does not reduce the influence of outliers; extreme values remain extreme relative to the distribution.
What should I do if I get this MLS-C01 question wrong?
Review winsorization, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Winsorization
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Last reviewed: Jun 20, 2026
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