MLA-C01 Data Preparation for Machine Learning Practice Question
A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?
⚠ Common exam trap
AWS often tests the misconception that one-hot encoding is always safe for categorical features, but the trap here is that high-cardinality categories require a dimensionality-reduction technique like target encoding, not a naive expansion that breaks scalability.
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
✓
Target encoding with smoothing using SageMaker Data Wrangler
Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hash encoding using Apache Spark on Amazon EMR
Why it's wrong here
Hash encoding may cause collisions and lose interpretability, though scalable.
- ✗
One-hot encoding using SageMaker Processing with scikit-learn
Why it's wrong here
One-hot encoding with 1000+ categories produces a very wide dataset, inefficient for training.
- ✗
Label encoding using Pandas in a SageMaker notebook
Why it's wrong here
Label encoding imposes arbitrary ordinal relationships, misleading tree-based models.
- ✓
Target encoding with smoothing using SageMaker Data Wrangler
Why this is correct
Target encoding reduces cardinality and is effective for tree models; Data Wrangler integrates natively.
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