Question 245 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer needs to handle missing values in a dataset containing numerical features. The missingness is completely at random (MCAR). Which imputation strategy is most robust for downstream model performance?
⚠ Common exam trap
AWS often tests the misconception that model-based imputation (Option C) is always superior, but the trap is that for MCAR data, simpler methods like median imputation are more robust and avoid overfitting, while model-based approaches can introduce unnecessary complexity and bias.
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 with median of each feature
When missingness is completely at random (MCAR), imputing with the median is robust because it preserves the central tendency of the distribution without introducing bias or distorting variance. Unlike mean imputation, the median is resistant to outliers, making it a safe default for numerical features in downstream models that assume normally distributed inputs or are sensitive to skewed data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Impute with median of each feature
Why this is correct
Median is robust to outliers and maintains the central tendency.
- ✗
Impute with a constant like -1
Why it's wrong here
A constant can shift the distribution and create artificial patterns.
- ✗
Use a model to predict missing values
Why it's wrong here
Model-based imputation can introduce bias and is computationally intensive for simple MCAR scenarios.
- ✗
Remove all rows with missing values
Why it's wrong here
Removing rows reduces sample size and can lose valuable information.
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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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