The correct answer is to impute missing values with the median of each feature and apply a log transformation to the target variable. This combination works because median imputation is robust to outliers that often accompany skewed data, preserving the underlying distribution of each feature without introducing bias, while the log transformation compresses the right-skewed target into a more normal distribution. Linear regression algorithms, including SageMaker’s built-in linear learner, assume normally distributed errors; transforming the target helps satisfy this assumption, leading to better convergence and more accurate predictions. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of data preparation trade-offs—specifically, why median beats mean for imputation when features may contain outliers, and why log transformation is preferred over other scalers for a skewed target. A common trap is choosing mean imputation or a standard scaler on the target, which would fail to handle skew. Memory tip: “Median for messy features, log for lopsided targets.”
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 data scientist is using SageMaker built-in linear learner algorithm for a regression problem. The dataset has 10 features, some have missing values, and the target variable is right-skewed. The data scientist wants to handle missing values and transform the target variable to improve model performance. Which data preparation steps should the data scientist take?
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 with the median of each feature and apply a log transformation to the target variable.
Option C is correct because imputing missing values with the median is robust to outliers and preserves the distribution of each feature, which is important when the target is right-skewed. Applying a log transformation to the right-skewed target variable helps normalize its distribution, which aligns with the linear learner algorithm's assumption of normally distributed errors and improves convergence and prediction accuracy.
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 one-hot encoding to all features and remove missing values by dropping rows.
Why it's wrong here
One-hot is not needed for all numeric features; dropping rows loses data.
✗
Standardize all features to have zero mean and unit variance, then apply a box-cox transformation to the target.
Why it's wrong here
Standardization is good, but box-cox requires positive values; log is simpler.
✓
Impute missing values with the median of each feature and apply a log transformation to the target variable.
Why this is correct
Handles missing values and skew appropriately.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove rows with missing values and normalize the target to range [0,1].
Why it's wrong here
Removing rows reduces data; normalization of target may not address skew.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume standardizing features (Option B) is always required, but for a right-skewed target, transforming the target itself (e.g., log transform) is more critical than scaling features, and imputation is essential to avoid data loss.
Detailed technical explanation
How to think about this question
The linear learner algorithm in SageMaker uses stochastic gradient descent (SGD) and assumes that the target variable follows a distribution with constant variance (homoscedasticity). A log transformation on a right-skewed target stabilizes variance and makes the relationship more linear, which directly improves the model's ability to converge to an optimal solution. Imputing with the median is preferred over the mean for features with missing values because the median is less sensitive to outliers, which are common in skewed datasets.
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 missing values with the median of each feature and apply a log transformation to the target variable. — Option C is correct because imputing missing values with the median is robust to outliers and preserves the distribution of each feature, which is important when the target is right-skewed. Applying a log transformation to the right-skewed target variable helps normalize its distribution, which aligns with the linear learner algorithm's assumption of normally distributed errors and improves convergence and prediction accuracy.
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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