DSA-C03 Exam Dumps | You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs

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Question 76/143

You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?

A. Log Loss, optimized with a grid search focusing on hyperparameters that improve overall accuracy.

B. Recall, optimized with a threshold adjustment to minimize false negatives.

C. Precision, optimized with a threshold adjustment to minimize false positives.

D. AUC-ROC, optimized with a randomized search focusing on hyperparameters related to model complexity.

E. F 1-Score, optimized to balance precision and recall equally.

Correct Answer: C

Precision is the most suitable optimization metric because it focuses on minimizing false positives. In fraud detection, incorrectly flagging legitimate transactions as fraudulent can have significant negative consequences for customers and the business. By optimizing for precision and adjusting the prediction threshold to further minimize false positives, you can ensure that the model identifies fraudulent transactions with a high degree of certainty. Recall would prioritize catching all fraud cases, even at the cost of increased false positives, which is not desirable in this scenario. While F1 balances precision and recall, the scenario specifically prioritizes precision. AUC-ROC is a good general measure of performance but does not directly address the specific requirement of minimizing false positives.

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