Question 553 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning team is preparing a dataset for a regression model. The dataset contains numerical features that are on different scales (e.g., age 0-100, income 0-1,000,000). The team plans to use Amazon SageMaker to train a linear regression model. Which THREE data preparation steps should the team take to ensure the model performs well? (Select THREE.)
⚠ Common exam trap
AWS often tests the misconception that feature selection or outlier removal are mandatory preprocessing steps for linear regression, when in fact scaling and handling missing values are the core requirements for model convergence and performance.
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
✓
Handle missing values by imputation or removal.
Missing values can cause errors or biased estimates in linear regression models. Amazon SageMaker's built-in linear regression algorithm does not handle missing data automatically, so imputation (e.g., mean/median) or removal is necessary to ensure the training process completes and produces reliable coefficients.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply feature selection to reduce the number of features.
Why it's wrong here
Feature selection is optional and not a mandatory step for all models.
- ✗
Remove outliers from the dataset.
Why it's wrong here
Outlier removal is not always required; it depends on the data and model.
- ✓
Handle missing values by imputation or removal.
Why this is correct
Missing values can cause errors or biased models; handling them is necessary.
- ✓
Encode categorical features using one-hot encoding.
Why this is correct
Linear regression requires numerical input; categorical features must be encoded.
- ✓
Scale numerical features using standardization (z-score) or normalization (min-max scaling).
Why this is correct
Linear models are sensitive to feature scales; scaling improves convergence and performance.
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Last reviewed: Jun 30, 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.
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