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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

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