Question 1,554 of 1,672
MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a model using the built-in Linear Learner algorithm. The training data contains missing values in some features. What is the best practice for handling missing values with this algorithm?
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 using mean or median imputation
Linear Learner expects dense input; it cannot handle missing values. The best practice is to impute missing values before training, such as using mean or median imputation. Removing rows with missing values (Option A) may lose valuable data. Setting missing values to zero (Option C) could bias the model. The algorithm does not have a built-in `handle_missing` parameter (Option D). Therefore, Option B (Impute missing values using mean or median imputation) is correct.
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 rows with missing values
Why it's wrong here
Removing rows with missing values is not best practice because it can discard useful data and reduce sample size.
- ✓
Impute missing values using mean or median imputation
Why this is correct
Imputing missing values using mean or median imputation is recommended because it preserves data and avoids bias.
- ✗
Set missing values to zero
Why it's wrong here
Setting missing values to zero can introduce bias and is not a standard approach for Linear Learner.
- ✗
Use the `handle_missing` parameter in the algorithm
Why it's wrong here
The Linear Learner algorithm does not have a `handle_missing` parameter; data preprocessing is required.
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Last reviewed: Jun 20, 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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