AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 76

An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

Answer options

• A. Increase the learning rate.
• B. Remove some irrelevant features from the training dataset.
• C. Increase the value of the max_depth hyperparameter.
• D. Decrease the value of the max_depth hyperparameter.

Correct answer: D

Explanation

The correct answer is D because reducing the max_depth hyperparameter can help prevent overfitting, allowing the model to generalize better to new data. Options A and C may exacerbate the overfitting issue, while option B, while potentially beneficial, does not directly address the model's complexity and its performance on unseen transactions.