DSA-C03 Exam Dumps | You are developing a regression model in Snowflake using Snowpark to predict house prices based on features

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Question 99/143

You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

A. Option A

B. Option B

C. Option C

D. Option D

E. Option E

Correct Answer: D

Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.

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