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MLS-C01 Modeling Practice Question

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?

⚠ Common exam trap

It's easy for candidates to confuse techniques that improve optimization (batch size, learning rate) with techniques that improve generalization (more data, stronger regularization), leading them to pick B or C instead of A.

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

Increase the amount of training data

Increasing the amount of training data provides the model with more examples of the underlying distribution, which helps reduce variance and combat overfitting. With 1 million rows and 100 features, the model may still be memorizing noise; adding more diverse data forces the linear regression to generalize better, improving validation R² without changing the model's capacity.

Answer analysis

Option-by-option breakdown

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

  • Increase the amount of training data

    Why this is correct

    More data helps the model generalize better.

  • Increase the batch size

    Why it's wrong here

    Larger batch size can lead to sharper minima and may not reduce overfitting.

  • Increase the learning rate

    Why it's wrong here

    Higher learning rate may cause divergence, not reduce overfitting.

  • Add more features

    Why it's wrong here

    More features increase model complexity and may worsen overfitting.

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