You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

• A. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
• B. Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
• C. Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
• D. Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
• E. Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.

A data scientist at 'Polaris Analytics' wants to estimate the average transaction value of all online purchases made during the Black Friday sale. Due to the enormous volume of data in Snowflake, they decide to use the Central Limit Theorem (CLT). They randomly sample 1000 transactions daily for 30 days and calculate the sample mean for each day. The sample mean values are stored in a Snowflake table named Which of the following SQL queries, assuming the table has a column of 'FLOAT' type, will provide the best estimate of the population mean and its confidence interval using the CLT?

• A. Option E
• B. Option C
• C. Option D
• D. Option B
• E. Option A

You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

• A. Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
• B. Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
• C. Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
• D. Increase the size of the training dataset significantly by querying data from external sources.
• E. Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.

You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

• A. Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
• B. Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.
• C. Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.
• D. Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
• E. Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.