- A
Normalization (L2)
Why wrong: Normalization scales by vector magnitude, not robust to outliers in each feature.
- B
Standardization (Z-score)
Why wrong: Standardization uses mean and standard deviation, which are influenced by outliers.
- C
Robust scaling
Robust scaling uses median and IQR, thus resilient to outliers.
- D
Min-max scaling
Why wrong: Min-max scaling is highly affected by outliers as it depends on min and max values.
Question 511 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Which feature scaling method is most robust to…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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?
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
✓
Robust scaling
Robust scaling is the most robust to outliers because it uses the median and interquartile range (IQR) instead of the mean and standard deviation. The median and IQR are not influenced by extreme values, so the scaled features remain stable even when the data contains significant outliers.
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.
- ✗
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
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that Standardization (Z-score) is robust because it uses standard deviation, but candidates forget that both mean and standard deviation are outlier-sensitive, whereas robust scaling explicitly uses median and IQR.
Detailed technical explanation
How to think about this question
Robust scaling subtracts the median and divides by the interquartile range (IQR = Q3 - Q1), making it resistant to outliers by design. In practice, this is critical for algorithms like PCA or k-means clustering where outlier-sensitive scaling can distort variance calculations or cluster centroids. A subtle behavior is that robust scaling does not guarantee zero mean or unit variance, which may affect algorithms that assume normally distributed features.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Robust scaling — Robust scaling is the most robust to outliers because it uses the median and interquartile range (IQR) instead of the mean and standard deviation. The median and IQR are not influenced by extreme values, so the scaled features remain stable even when the data contains significant outliers.
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.
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 →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.