You've built a regression model in Snowflake using Snowpark Python to predict customer churn. After evaluating the model on a holdout dataset, you generate a residuals plot. The plot shows a distinct 'U' shape. Which of the following interpretations and subsequent actions are most appropriate?

• A. The 'U' shape suggests the model is missing important non-linear relationships. Consider adding polynomial features or using a non-linear model like a Random Forest or Gradient Boosting Machine.
• B. The 'U' shape indicates homoscedasticity. No changes to the model are necessary.
• C. The 'U' shape implies multicollinearity is present. Use techniques like Variance Inflation Factor (VIF) to identify and remove highly correlated features.
• D. The 'U' shape suggests that the learning rate is too high. Reduce the learning rate of the model.
• E. The 'U' shape indicates that the residuals are normally distributed. This is a positive sign and no changes are required.

You are using the Snowflake Python connector from within a Jupyter Notebook running in VS Code to train a model. You have a Snowflake table named 'CUSTOMER DATA' with columns 'ID', 'FEATURE 1', 'FEATURE_2, and 'TARGET. You want to efficiently load the data into a Pandas DataFrame for model training, minimizing memory usage. Which of the following code snippets is the MOST efficient way to achieve this, assuming you only need 'FEATURE 1', 'FEATURE 2, and 'TARGET' columns?

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You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

• A. Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.
• B. Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.
• C. Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using
• D. Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.
• E. Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.

You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

• A. Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
• B. Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
• C. Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).
• D. Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).
• E. Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.