The answer is to scale numerical features using standardization or normalization, handle missing values through imputation or removal, and ensure the target variable is appropriately distributed. These three data preparation steps for linear regression are critical because the algorithm assumes features contribute equally to the prediction; when features like age (0-100) and income (0-1,000,000) are on different scales, the model’s coefficients become biased toward high-magnitude features, undermining performance. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of SageMaker’s built-in linear learner, which does not automatically standardize data or handle nulls—a common trap is forgetting that missing values cause training failures or skewed coefficients. A reliable memory tip is “Scale, Clean, Check”: always scale features, clean missing data, and check target distribution to avoid scale-induced bias and runtime errors.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 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.)
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.
Option C is correct because 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.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
Encode categorical features using one-hot encoding.
Why this is correct
Linear regression requires numerical input; categorical features must be encoded.
Related concept
Read the scenario before looking for a memorised answer.
✓
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.
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 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.
Detailed technical explanation
How to think about this question
Standardization (z-score) transforms features to have mean 0 and variance 1, which is essential for linear regression because coefficients are sensitive to feature scales—without scaling, features with larger magnitudes dominate the loss function. SageMaker's linear learner algorithm internally applies normalization by default, but explicit scaling ensures consistency and interpretability of coefficients. One-hot encoding converts categorical variables into binary columns, avoiding the false ordinal relationships that would bias the model if categories were encoded as integers.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Handle missing values by imputation or removal. — Option C is correct because 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.
What should I do if I get this MLA-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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