DSA-C03 Exam Dumps | You are training a binary classification model in Snowflake to predict customer churn using Snowpark

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Snowflake.DSA-C03.v2026-01-08.q143
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Question 14/143

You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

A. Accuracy - as it measures the overall correctness of the model.

B. Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.

C. Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.

D. Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.

E. F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.

Correct Answer: D,E

AUC-ROC is suitable because it evaluates the model's ability to discriminate between classes regardless of class imbalance. F1-Score balances precision and recall, which is crucial for imbalanced datasets to avoid models biased towards the majority class. Log Loss is also a good option but less robust to class imbalance than AUC-ROC. Accuracy is inappropriate due to class imbalance, and RMSE is for regression problems.

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