Exam DSA-C03 Topic 1 Question 154 Discussion

Actual exam question for Snowflake's DSA-C03 exam
Question #: 154
Topic #: 1

You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?

A. Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift. B. Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold. C. Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes. D. Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay. E. Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.

Suggested Answer: A,B Vote an answer

Options A and B are the most effective. A identifies data drift by comparing feature distributions, indicating potential changes in the input data. B monitors performance metrics and triggers alerts based on predefined thresholds. C is too manual and inefficient. D isn't appropriate, re-running model training would produce a new model not identifying degradation of current one. E is premature; further investigation is necessary. Assessing the performance is import after significant drops found for retraining of the model with latest data.

by Ruth at Sep 30, 2025, 12:13 AM

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