Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

Precision measures the proportion of true positive predictions out of all positive predictions made by the model. In this scenario, since the cost of a false positive is high, it is critical to minimize false positives, which means prioritizing high precision.

From AWS documentation:

“Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. High precision relates to a low false positive rate.”

— AWS ML Exam Guide – Evaluation Metrics

Recall (C) would focus on minimizing false negatives, which is not important here as missing positives has no cost. F1 (D) balances precision and recall, but the question emphasizes prioritizing precision only.

Updating the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant is the simplest approach with the least risk to deploy the model and roll it back, if needed. This is because SageMaker supports A/B testing, which allows the Specialist to compare the performance of different model variants by sending a portion of the traffic to each variant. The Specialist can monitor the metrics of each variant and adjust the weights accordingly. If the new variant does not perform as expected, the Specialist can revert traffic to the last version by resetting the weights to 100% for the old variant and 0% for the new variant. This way, the Specialist can deploy the model without affecting the customer experience and roll it back easily if needed. References:

Amazon SageMaker

Deploying models to Amazon SageMaker hosting services

The machine learning model in this scenario shows signs of overfitting, as evidenced by better performance on the training dataset than on the testing dataset. Overfitting indicates that the model is capturing noise or details specific to the training data rather than general patterns.

One common approach to reduce overfitting is L2 regularization, which adds a penalty to the loss function for large weights and helps the model generalize better by smoothing out the weight distribution. By increasing the value of the L2 hyperparameter, the ML specialist can increase this penalty, helping to mitigate overfitting and improve performance on the testing dataset.

Options like increasing momentum or reducing dropout are less effective for addressing overfitting in this context.