MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to train a model on a dataset with many categorical features. They want to use SageMaker's built-in Linear Learner algorithm. What preprocessing step is required for the categorical features?
⚠ Common exam trap
A common mix-up: candidates confuse label encoding (assigning integers) with one-hot encoding, assuming any numerical conversion suffices, but label encoding introduces false ordinality that degrades linear model performance.
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
✓
Apply one-hot encoding to convert them to numerical vectors.
The SageMaker Linear Learner algorithm requires numerical input features. Categorical features must be converted to numerical vectors, typically via one-hot encoding, because the algorithm performs linear regression or classification on numerical data. Without this preprocessing, the algorithm cannot interpret categorical values directly.
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 convert them to numerical vectors.
Why this is correct
Linear models need numerical features; one-hot encoding is standard.
- ✗
Use label encoding to assign integers to categories.
Why it's wrong here
Label encoding implies order, which can mislead linear models.
- ✗
Normalize the categorical features using min-max scaling.
Why it's wrong here
Normalization applies to numerical features; categorical need encoding first.
- ✗
Remove categorical features with high cardinality.
Why it's wrong here
Unnecessary; encoding handles cardinality.
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