The answer is to impute missing values with the median of each numerical feature. When missingness is completely at random (MCAR), the median is the most robust choice because it preserves the central tendency of the distribution without being influenced by outliers, unlike the mean which can shift the data and distort variance. This makes median imputation particularly safe for downstream models that assume normally distributed inputs or are sensitive to skewed data, as it avoids introducing systematic bias. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of how different imputation strategies affect model robustness under MCAR conditions—a common trap is choosing mean imputation, which seems intuitive but fails when outliers are present. Remember the memory tip: "Median for MCAR, Mean for misery" — median handles randomness without skewing your model’s assumptions.
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 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?
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.
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.
✓
Impute with median of each feature
Why this is correct
Median is robust to outliers and maintains the central tendency.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Scenario analysis trap
Model-based imputation can introduce bias and is computationally intensive for simple MCAR scenarios.
Detailed technical explanation
How to think about this question
Under MCAR, the missing values are a simple random sample of all data points, so any unbiased imputation method that preserves the distribution's shape is valid. Median imputation is preferred over mean imputation because it is less influenced by extreme values, which is critical when the feature distribution is skewed or contains outliers—common in real-world datasets like income or sensor readings. In practice, median imputation is often combined with a missing indicator column to allow the model to learn potential patterns from the missingness itself, though this is not required under strict MCAR.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: 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.
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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