- A
Apply log transformation.
Log transformation stabilizes variance when variance increases with mean.
- B
Standardize the features using Z-scores.
Why wrong: Standardization does not fix heteroscedasticity.
- C
Apply a sine transformation.
Why wrong: Sine transformation is not for variance stabilization.
- D
Apply Box-Cox transformation with lambda=0.
Why wrong: Box-Cox with lambda=0 is log, but the question asks for appropriate; log is more straightforward.
Question 1,391 of 1,755
Log Transformation: Stabilizing Variance in EDA
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 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?
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
✓
Apply log transformation.
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.
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 log transformation.
Why this is correct
Log transformation stabilizes variance when variance increases with mean.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Standardize the features using Z-scores.
Why it's wrong here
Standardization does not fix heteroscedasticity.
- ✗
Apply a sine transformation.
Why it's wrong here
Sine transformation is not for variance stabilization.
- ✗
Apply Box-Cox transformation with lambda=0.
Why it's wrong here
Box-Cox with lambda=0 is log, but the question asks for appropriate; log is more straightforward.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the distinction between transformations that stabilize variance (log, Box-Cox) versus those that only standardize (Z-scores) or are domain-specific (sine), and candidates may incorrectly choose Box-Cox with lambda=0 thinking it is a separate technique, missing that the log transformation is the canonical answer for funnel-shaped heteroscedasticity.
Detailed technical explanation
How to think about this question
The log transformation is a variance-stabilizing transformation (VST) derived from the Taylor series expansion, where if the standard deviation is proportional to the mean, the log makes the variance approximately constant. In practice, this is common in financial data (e.g., stock prices) or biological counts, where multiplicative errors dominate. The Box-Cox transformation generalizes this with lambda=0 for logs, but the log transformation is the direct and simplest choice for this pattern.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply log transformation. — 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.
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.
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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
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?
medium- A.Min-max scaling
- ✓ B.Log transformation
- C.Standardization (z-score)
- D.Binning
Why 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. 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?
medium- ✓ A.Apply log transformation (log(Price)).
- B.Apply square transformation (Price^2).
- C.Apply min-max scaling to the 'Price' column.
- D.Bin the 'Price' values into equal-width intervals.
Why A: Option A is correct because log transformation is commonly applied to right-skewed data to reduce skewness and make the distribution more normal, which is suitable for linear models assuming normally distributed errors. Option B (square transformation) exacerbates skewness, making it worse. Option C (min-max scaling) only rescales the data to a fixed range and does not change the shape of the distribution. Option D (binning) discards information and does not transform the distribution to be normal.
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Last reviewed: Jun 11, 2026
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