Question 650 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
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?
⚠ Common exam trap
Candidates often 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.
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 missing values with the median of each feature and apply a log transformation to the target variable.
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.
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
Standardising all features is unnecessary for linear learner, which handles varying scales natively, and does not address the missing values present in the dataset. The box-cox transformation is tempting because it is designed to reduce right skew in the target, but it fails here because it requires all values to be positive, whereas the stem does not guarantee that condition. This option would be correct only if the dataset had no missing values and the target contained only positive values.
- ✓
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.
- ✗
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.
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Last reviewed: Jun 24, 2026
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