AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 167

An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features.

Which solution will meet these requirements?

Answer options

• A. Increase the number of training iterations. Retrain the model.
• B. Apply L1 regularization to the training data. Retrain the model.
• C. Decrease the number of training iterations. Retrain the model.
• D. Use SageMaker Debugger to apply L1 regularization to the running model.

Correct answer: B

Explanation

Applying L1 regularization is an effective method to reduce overfitting by penalizing the absolute size of the coefficients of the model, effectively driving some of them to zero and eliminating unnecessary features. Increasing or decreasing training iterations does not address overfitting directly, while using SageMaker Debugger for L1 regularization on a running model is less common than applying it directly to the training data before retraining.