Question 1,497 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?
⚠ Common exam trap
A common mix-up: candidates assume linear regression can inherently handle missing values (Option A) due to its statistical robustness, but AWS SageMaker's implementation requires complete data, and ignoring missing values will cause runtime errors or silent model degradation.
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
✓
Impute missing values with the mean of the column.
Linear regression models in Amazon SageMaker cannot handle missing values natively; they require complete numerical input. Imputing missing values with the column mean is a standard preprocessing technique that preserves the overall distribution and avoids introducing bias, ensuring the SageMaker built-in Linear Learner algorithm can train without errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore missing values; linear regression can handle them.
Why it's wrong here
Linear regression cannot handle missing values.
- ✓
Impute missing values with the mean of the column.
Why this is correct
Imputation is a common technique to handle missing data.
- ✗
Replace missing values with zeros.
Why it's wrong here
This may introduce bias; mean imputation is often better.
- ✗
Remove all rows containing missing values.
Why it's wrong here
This may reduce dataset size and lose information.
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Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
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