MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer needs to prepare a dataset for a fraud detection model. The dataset contains a highly skewed numerical feature with extreme outliers. The engineer decides to apply a logarithmic transformation to this feature before training. Which SageMaker Data Wrangler transform should be used to apply the logarithmic transformation?
⚠ Common exam trap
Watch out — candidates often confuse scaling transforms with distribution-changing transforms; scaling does not alter skewness or outliers.
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
✓
Use the 'Log Transform' transform in Data Wrangler.
The logarithmic transformation is a common technique to reduce right skewness and stabilize variance in numerical data. In SageMaker Data Wrangler, the 'Log Transform' transform applies a natural logarithm to the selected column, effectively compressing the scale of large values and making the distribution more symmetric. This helps models that are sensitive to feature distributions, such as linear models, to perform better. Other transforms like scaling or encoding do not change the distribution shape.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'Standard Scaler' transform in Data Wrangler.
Why it's wrong here
The 'Standard Scaler' transform standardizes features by removing the mean and scaling to unit variance. While it helps with features on different scales, it does not change the shape of the distribution or compress outliers. It would not address the skewness and extreme outliers described, making it unsuitable for this scenario.
- ✗
Use the 'One-Hot Encoding' transform in Data Wrangler.
Why it's wrong here
The 'One-Hot Encoding' transform is used for categorical variables, converting them into binary vectors. It is not applicable to numerical features and does not perform any mathematical transformation to reduce skewness or handle outliers. Therefore, it is incorrect for this numerical feature scenario.
- ✓
Use the 'Log Transform' transform in Data Wrangler.
Why this is correct
The 'Log Transform' transform in SageMaker Data Wrangler applies a natural logarithm (base e) to the selected numeric column. This is specifically designed to reduce right skewness and mitigate the impact of extreme outliers, making the feature more suitable for models that assume normality or are sensitive to scale. It directly addresses the scenario's need for a logarithmic transformation.
- ✗
Use the 'Min-Max Scaler' transform in Data Wrangler.
Why it's wrong here
The 'Min-Max Scaler' transform rescales features to a fixed range, typically [0, 1]. This is sensitive to outliers because the range is determined by the minimum and maximum values. Extreme outliers would compress the rest of the data into a small interval, potentially harming model performance. It does not reduce skewness.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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