Question 889 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset has 500 features and 1 million rows. After training, the model's training RMSE is 2.5 and validation RMSE is 2.6, which is acceptable. However, the scientist notices that many feature coefficients are very small but non-zero, and the model takes a long time to train. The scientist wants to reduce training time while maintaining similar accuracy. Which action should the scientist take?
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 L1 regularization strength
(increase L1 regularization) will drive many coefficients to zero, reducing effective features and thus training time. Option A (increase mini-batch size) may speed training but could affect convergence. Option C (switch to a neural network model) is unnecessary for this task. Option D (increase L2 regularization) shrinks coefficients but doesn't zero them out, so less impact on training speed.
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 mini-batch size
Why it's wrong here
Increasing mini-batch size may speed up training by processing more data per batch, but it can affect convergence and does not address the small coefficient issue.
- ✓
Increase the L1 regularization strength
Why this is correct
Increasing L1 regularization drives many coefficients to zero, reducing the effective number of features and speeding up training, while maintaining similar accuracy.
- ✗
Switch to a neural network model
Why it's wrong here
Switching to a neural network is unnecessary as the linear regression model already performs well.
- ✗
Increase the L2 regularization strength
Why it's wrong here
Increasing L2 regularization shrinks coefficients but does not zero them out completely, so it has less impact on training speed compared to L1.
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Last reviewed: Jun 20, 2026
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