hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Data…
A data scientist is using Amazon SageMaker Data Wrangler to create a feature engineering pipeline for a dataset with both numeric and categorical features. The scientist wants to apply transformations that are appropriate for a linear model. Which THREE transformations should the scientist apply? (Choose THREE.)
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
✓
Remove features with high pairwise correlation
Linear models assume features are numeric, scaled, and not highly correlated. StandardScaler ensures all numeric features have comparable scales. One-hot encoding converts categorical features into binary columns without imposing ordinality. Removing highly correlated features reduces multicollinearity which can destabilize coefficient estimates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MinMaxScaler on numeric features
Why it's wrong here
MinMaxScaler is also acceptable, but StandardScaler is more commonly used for linear models. However, the question asks for THREE; the combination of StandardScaler, one-hot encoding, and correlation removal is the best set. MinMaxScaler could work but is not uniquely required.
- ✓
Remove features with high pairwise correlation
Why this is correct
High correlation between features can cause multicollinearity, making linear model coefficients unstable and hard to interpret.
- ✓
One-hot encoding on categorical features
Why this is correct
One-hot encoding is appropriate for linear models to avoid implying ordinal relationships among categories.
- ✗
Label encoding on categorical features
Why it's wrong here
Label encoding introduces ordinality that can mislead linear models; one-hot encoding is preferred.
- ✓
StandardScaler on numeric features
Why this is correct
Scaling is essential for linear models so that features with larger magnitudes do not dominate the loss function.
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