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AI Models and Data EngineeringeasyMultiple SelectObjective-mapped

AI0-001 AI Models and Data Engineering Practice Question

A data scientist is preparing a dataset for a classification model. The dataset contains several categorical variables with high cardinality. Which TWO encoding methods are appropriate for converting these categorical variables into numerical features?

⚠ Common exam trap

Candidates may incorrectly discard label encoding (option E) because they assume it always imposes an ordinal relationship, but in many classification contexts (e.g., tree-based models) label encoding can handle high-cardinality nominal variables without issue. The trap is to overgeneralize the rule against label encoding, even when the question lists it as a correct answer alongside one-hot encoding.

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

One-hot encoding

One-hot encoding is appropriate for high-cardinality categorical variables because it creates binary columns for each category, allowing the model to treat each category as an independent feature without imposing an ordinal relationship. This is crucial for classification models that assume numerical inputs, as it prevents the model from misinterpreting arbitrary integer labels as having meaningful order or magnitude.

Answer analysis

Option-by-option breakdown

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

  • Min-max scaling

    Why it's wrong here

    Min-max scaling is a normalization method for numerical features, not for encoding categorical variables.

  • K-means clustering

    Why it's wrong here

    K-means is a clustering algorithm, not an encoding technique.

  • One-hot encoding

    Why this is correct

    One-hot encoding converts each category into a binary vector, suitable for categorical variables.

  • Principal component analysis (PCA)

    Why it's wrong here

    PCA is a dimensionality reduction technique, not an encoding method.

  • Label encoding

    Why this is correct

    Label encoding assigns a unique integer to each category, appropriate for ordinal or high-cardinality categorical variables.

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