MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist is working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)

⚠ Common exam trap

AWS often tests the distinction between techniques that are universally applicable (like PCA for numerical features) versus those that are specifically designed to handle high-cardinality categorical variables (like target encoding and frequency encoding), tempting candidates to choose one-hot encoding without considering its impracticality for high cardinality.

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

✓

Principal Component Analysis (PCA) to reduce dimensionality of numerical features.

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Principal Component Analysis (PCA) to reduce dimensionality of numerical features.
Why this is correct
PCA can reduce noise and multicollinearity.
✗
Domain-specific feature engineering based on business rules.
Why it's wrong here
This is often useful but not a general technique applicable to all datasets.
✓
Target encoding for high-cardinality categorical variables.
Why this is correct
Target encoding replaces each category with the mean target value, useful for high cardinality.
✓
Frequency encoding to represent categories by their occurrence count.
Why this is correct
Frequency encoding summarizes category prevalence.
✗
One-hot encoding all categorical features.
Why it's wrong here
One-hot encoding high cardinality features creates a large sparse matrix.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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