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AIF-C01 Applications of Foundation Models Practice Question

A data scientist is fine-tuning a foundation model on SageMaker. They want to prevent overfitting. Which THREE actions can help? (Select THREE.)

⚠ Common exam trap

AWS often tests the misconception that increasing epochs or using a smaller learning rate directly prevents overfitting, when in fact these are hyperparameter tuning strategies that can exacerbate or fail to address overfitting without explicit regularization.

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

Apply dropout

Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. In SageMaker, dropout can be applied via framework-specific APIs (e.g., `tf.keras.layers.Dropout` in TensorFlow) or by configuring the model architecture in the training script.

Answer analysis

Option-by-option breakdown

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

  • Apply dropout

    Why this is correct

    Dropout prevents co-adaptation of neurons.

  • Increase training data size

    Why this is correct

    More data helps generalize better.

  • Increase the number of epochs

    Why it's wrong here

    More epochs often lead to overfitting.

  • Use early stopping

    Why this is correct

    Stops training before overfitting.

  • Use a smaller learning rate

    Why it's wrong here

    Smaller learning rate may not prevent overfitting directly.

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