MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 dataset contains a numerical feature with extreme outliers. The outliers are genuine (not errors), and the ML model is a linear regression which is sensitive to outliers. Which data transformation should be applied to reduce the impact of outliers while preserving the data?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Robust scaling (median and IQR)
Robust scaling uses the median and interquartile range (IQR) to center and scale the data, making it resistant to extreme outliers. Since linear regression is sensitive to outliers, this transformation reduces their influence while preserving the original data distribution, unlike methods that rely on mean and variance.
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 scaling
Why it's wrong here
Min-max scaling is affected by min and max values, which can be outliers.
✗
Log transformation
Why it's wrong here
Log transformation reduces skew but does not eliminate outlier influence on scaling.
✓
Robust scaling (median and IQR)
Why this is correct
Robust scaling uses median and interquartile range, not affected by extreme values.
Related concept
Read the scenario before looking for a memorised answer.
✗
Standardization (z-score)
Why it's wrong here
Standardization uses mean and standard deviation, both influenced by outliers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between scaling methods that are robust to outliers versus those that are not, trapping candidates who assume all normalization techniques handle outliers equally.
Detailed technical explanation
How to think about this question
Robust scaling subtracts the median (a robust measure of central tendency) and divides by the IQR (the range between the 25th and 75th percentiles), which is unaffected by extreme values. This ensures that outliers do not dominate the scaling process, making it ideal for models like linear regression that assume normally distributed features without extreme leverage points. In practice, this transformation is often applied before training to prevent a single outlier from skewing the regression coefficients.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Robust scaling (median and IQR) — Robust scaling uses the median and interquartile range (IQR) to center and scale the data, making it resistant to extreme outliers. Since linear regression is sensitive to outliers, this transformation reduces their influence while preserving the original data distribution, unlike methods that rely on mean and variance.
What should I do if I get this MLA-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.
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Question Discussion
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