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MLS-C01 Modeling Practice Question

Which THREE factors should be considered when choosing between a parametric and a non-parametric machine learning model?

⚠ Common exam trap

AWS often tests the misconception that non-parametric models are less prone to overfitting because they are 'simpler,' when in fact their flexibility makes them more susceptible to overfitting without careful tuning or large datasets.

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

Non-parametric models are generally more flexible

Non-parametric models, such as k-nearest neighbors or decision trees, do not assume a fixed functional form for the data, allowing them to capture complex, non-linear relationships. This flexibility makes them well-suited for datasets where the underlying distribution is unknown or highly irregular, but it also increases the risk of overfitting if not properly regularized.

Answer analysis

Option-by-option breakdown

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

  • Non-parametric models are generally more flexible

    Why this is correct

    Non-parametric models can fit complex patterns.

  • Parametric models have lower bias than non-parametric models

    Why it's wrong here

    Parametric models often have higher bias.

  • Parametric models train faster than non-parametric models

    Why this is correct

    Non-parametric models often require more computation.

  • Non-parametric models are less prone to overfitting

    Why it's wrong here

    Non-parametric models can overfit easily.

  • Parametric models typically require less training data

    Why this is correct

    Parametric models have fixed number of parameters.

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This MLS-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 MLS-C01 exam.