A machine learning (ML) engineer is creating a binary classification model.

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.