- A
Min-max normalization
Why wrong: Min-max scaling preserves the relative distances and does not reduce outlier influence.
- B
Box-Cox transformation
Why wrong: Box-Cox assumes positive values and may not adequately handle extreme outliers.
- C
Rank transformation
Rank transformation replaces values with their rank order, making the distribution uniform and robust to outliers.
- D
Log transformation
Why wrong: Log transformation reduces skew but large outliers still have a significant impact.
Question 303 of 1,755
MLS-C01 Exploratory Data Analysis Practice Question
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 machine learning engineer is analyzing a dataset for a regression problem. The target variable has a long-tail distribution with extreme outliers. The engineer wants to reduce the influence of outliers while preserving the relative order of values. Which data transformation should the engineer apply to the target variable?
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
✓
Rank transformation
The rank transformation is correct because it maps each value to its rank in the dataset, preserving the relative order of values while eliminating the impact of magnitude differences. This effectively reduces the influence of extreme outliers without distorting the ordinal relationships. Option A (min-max normalization) is incorrect because it linearly scales values to a fixed range, and outliers can still dominate the scaling. Option B (Box-Cox transformation) can reduce skew but requires positive values and does not fully remove outlier influence; it transforms the distribution shape but still allows extreme values to affect the transformation parameters. Option D (log transformation) reduces right skew but remains monotonic, so extreme high values still have a disproportionate effect on the model compared to rank transformation.
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.
- ✗
Min-max normalization
Why it's wrong here
Min-max scaling preserves the relative distances and does not reduce outlier influence.
- ✗
Box-Cox transformation
Why it's wrong here
Box-Cox assumes positive values and may not adequately handle extreme outliers.
- ✓
Rank transformation
Why this is correct
Rank transformation replaces values with their rank order, making the distribution uniform and robust to outliers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Log transformation
Why it's wrong here
Log transformation reduces skew but large outliers still have a significant impact.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
Got this wrong? Here's your next step.
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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Exploratory Data Analysis — study guide chapter
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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: Rank transformation — The rank transformation is correct because it maps each value to its rank in the dataset, preserving the relative order of values while eliminating the impact of magnitude differences. This effectively reduces the influence of extreme outliers without distorting the ordinal relationships. Option A (min-max normalization) is incorrect because it linearly scales values to a fixed range, and outliers can still dominate the scaling. Option B (Box-Cox transformation) can reduce skew but requires positive values and does not fully remove outlier influence; it transforms the distribution shape but still allows extreme values to affect the transformation parameters. Option D (log transformation) reduces right skew but remains monotonic, so extreme high values still have a disproportionate effect on the model compared to rank transformation.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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