Quick Answer
The answer is robust scaling, the feature scaling method most robust to outliers. This technique resists distortion from extreme values because it centers the data using the median and scales it using the interquartile range (IQR), specifically the 25th and 75th percentiles. Unlike standardization, which relies on the mean and standard deviation—both easily skewed by outliers—or min-max scaling, which is entirely dependent on the minimum and maximum extremes, robust scaling remains stable even when your dataset contains significant anomalies. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept often appears in questions about data preprocessing for linear models or distance-based algorithms like K-means, where outliers can heavily bias the results. A common trap is assuming standardization is always safe; remember that if your data has outliers, robust scaling is the safer default. For a quick memory tip, think of the median and IQR as the “robust” statistics—they ignore the extremes, just like a robust model should.
This MLA-C01 practice question tests your understanding of ml model development. 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.
Which feature scaling method is most robust to outliers in the data?
Normalization (L2)
Why wrong: Normalization scales by vector magnitude, not robust to outliers in each feature.
Standardization (Z-score)
Why wrong: Standardization uses mean and standard deviation, which are influenced by outliers.
Robust scaling
Robust scaling uses median and IQR, thus resilient to outliers.
Min-max scaling
Why wrong: Min-max scaling is highly affected by outliers as it depends on min and max values.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Robust scaling
Robust scaling uses median and interquartile range, making it less sensitive to outliers than standardization (mean and standard deviation) or min-max scaling (range dependent on extremes).
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.
Normalization (L2)
Why it's wrong here
Normalization scales by vector magnitude, not robust to outliers in each feature.
Standardization (Z-score)
Why it's wrong here
Standardization uses mean and standard deviation, which are influenced by outliers.
Robust scaling
Why this is correct
Robust scaling uses median and IQR, thus resilient to outliers.
Related concept
Read the scenario before looking for a memorised answer.
Min-max scaling
Why it's wrong here
Min-max scaling is highly affected by outliers as it depends on min and max values.
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 MLA-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
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Robust scaling — Robust scaling uses median and interquartile range, making it less sensitive to outliers than standardization (mean and standard deviation) or min-max scaling (range dependent on extremes).
Identify which MLA-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.
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Last reviewed: Jun 23, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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