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Fundamentals of AI and MLmediumMultiple ChoiceObjective-mapped

AIF-C01 Fundamentals of AI and ML Practice Question

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

⚠ Common exam trap

The AIF-C01 exam often tests the distinction between feature reduction (PCA) and feature transformation (scaling, encoding, polynomial expansion) to see if candidates confuse techniques that change feature count versus those that only change feature values.

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)

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

Answer analysis

Option-by-option breakdown

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

  • Feature scaling

    Why it's wrong here

    Scaling normalizes values but does not reduce number of features.

  • One-hot encoding

    Why it's wrong here

    One-hot encoding increases the number of features, not reduces.

  • Principal Component Analysis (PCA)

    Why this is correct

    PCA reduces dimensionality by transforming correlated features into uncorrelated components, eliminating redundancy.

  • Polynomial features

    Why it's wrong here

    Polynomial features increase dimensionality, making redundancy worse.

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This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.