This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is using Amazon SageMaker to train a linear regression model. The training data contains outliers. Which THREE techniques can mitigate the impact of outliers?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove observations with outlier values from the dataset.
Option A is correct because removing observations with outlier values directly eliminates data points that can disproportionately influence the linear regression coefficients, leading to a more stable and representative model. In Amazon SageMaker, this can be done during data preprocessing using built-in algorithms or custom scripts in a SageMaker Processing job.
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.
✓
Remove observations with outlier values from the dataset.
Why this is correct
Direct removal eliminates outlier impact.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of layers in the model.
Why it's wrong here
More complexity may overfit to outliers.
✗
Standardize the features to have mean zero and unit variance.
Why it's wrong here
Standardization does not reduce outlier influence.
✓
Apply winsorization to the feature values.
Why this is correct
Winsorization limits extreme values to reduce outlier influence.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a loss function that is robust to outliers, such as Huber loss.
Why this is correct
Huber loss combines MSE and MAE, less sensitive to outliers.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that feature scaling (standardization) alone can handle outliers, but scaling does not reduce the leverage of extreme values; it only changes their numeric range.
Detailed technical explanation
How to think about this question
Huber loss, used in Option E, combines squared loss for small residuals and absolute loss for large residuals, making it less sensitive to outliers than mean squared error. Winsorization (Option D) caps extreme values at specified percentiles, effectively limiting their influence without removing data points. Both techniques are robust alternatives that can be implemented in SageMaker's built-in linear learner algorithm by customizing the loss function or preprocessing data.
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
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
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove observations with outlier values from the dataset. — Option A is correct because removing observations with outlier values directly eliminates data points that can disproportionately influence the linear regression coefficients, leading to a more stable and representative model. In Amazon SageMaker, this can be done during data preprocessing using built-in algorithms or custom scripts in a SageMaker Processing job.
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.
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Question Discussion
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