DSA-C03 Exam Dumps | You have a regression model deployed in Snowflake predicting customer churn probability, and you're using

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Snowflake.DSA-C03.v2026-01-08.q143
Question 31

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Question 31/143

You have a regression model deployed in Snowflake predicting customer churn probability, and you're using RMSE to monitor its performance. The current production RMSE is consistently higher than the RMSE you observed during initial model validation. You suspect data drift is occurring. Which of the following are effective strategies for monitoring, detecting, and mitigating this data drift to improve RMSE? (Select TWO)

A. Implement a process to continuously calculate and track the RMSE on a holdout dataset representing the most recent data, alerting you when the RMSE exceeds a predefined threshold.

B. Regularly re-train the model on the entire historical dataset to ensure it captures all possible data patterns.

C. Use Snowflake's data lineage features to identify any changes in the upstream data sources feeding the model and assess their potential impact.

D. Randomly sample a large subset of the production data and manually compare it to the original training data to identify any differences.

E. Disable model monitoring, because the increased RMSE shows that the model is adapting to new patterns.

Correct Answer: A,C

Option A provides a proactive approach to monitoring the model's performance on new data and triggering alerts when the RMSE deteriorates. Option C helps identify changes in the input data that could be causing the drift. Option B is not ideal, as retraining on all historical data might not effectively adapt to recent drifts. Option D is inefficient and impractical for large datasets. Option E is incorrect because a high RMSE indicates poor performance and warrants investigation, not ignoring.

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