AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 179

A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.

Which solution will meet this requirement?

Answer options

• A. Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
• B. Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
• C. Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers. Implement cross-entropy loss functions.
• D. Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.

Correct answer: B

Explanation

The correct answer, B, suggests implementing dropout layers and regularization techniques, which can help prevent overfitting and improve the model's generalization to unseen data. Option A increases complexity, which may exacerbate overfitting, while C focuses on dimensionality reduction and loss function changes that do not directly address overfitting. Option D deals with dataset augmentation but does not specifically target model improvement techniques.