A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

• A. Increase the learning rate significantly to speed up convergence during training.
• B. Reduce the number of features by randomly removing some columns, as this always prevents overfitting.
• C. Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
• D. Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.
• E. Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.

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You are building a fraud detection model using Snowflake and discover a severe class imbalance (99% legitimate transactions, 1% fraudulent). You plan to use down-sampling to address this. Which of the following strategies and Snowflake SQL commands would be MOST effective and efficient for down-sampling the majority class (legitimate transactions) in a large Snowflake table named 'TRANSACTIONS before training a model using Snowpark?

• A. Create a new table 'BALANCED TRANSACTIONS' by sampling the majority class and combining it with the minority class using UNION ALLS. Use the'SAMPLE clause in Snowflake SQL for efficient sampling:
  
• B. Randomly delete rows from the 'TRANSACTIONS table where 'IS FRAUD = FALSE until the class distribution is balanced. This avoids data duplication but can be slow on large tables.
• C. Use Snowpark's function with replacement to create a balanced dataset. This is efficient within the Snowpark environment but might be slower than native SQL sampling for initial data preparation.
• D. Create a new table 'BALANCED_TRANSACTIONS' by sampling the majority class and combining it with the minority class using 'UNION ALL'. Use the 'SAMPLE clause in Snowflake SQL for efficient sampling:
  
• E. Manually iterate through the 'TRANSACTIONS' table using a Snowpark 'DataFrame' and randomly select rows from the majority class. This is the most efficient approach for very large tables.

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You're working with a large dataset of user transactions in Snowflake. You need to identify potential outliers in transaction amounts C TRANSACTION AMOUNT) for each user CUSER ID'). Your goal is to flag transactions that are more than 3 standard deviations away from the mean transaction amount for that specific user. Which of the following approaches, utilizing Snowflake's statistical functions and window functions, would be MOST efficient and accurate for achieving this?

• A. Creating a stored procedure that iterates through each user and calculates the mean and standard deviation individually.
• B. Using a correlated subquery to calculate the mean and standard deviation for each user and then filtering the transactions.
• C. Exporting the data to a Python environment, performing the calculations using Pandas, and then re-importing the results to Snowflake.
• D. Using window functions to calculate the mean and standard deviation for each user within the same query, and then comparing each transaction amount to the calculated range.
• E. Calculating the overall mean and standard deviation for all transactions and filtering transactions based on those global statistics.

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You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

• A. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
• B. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
• C. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
• D. Increase the regularization strength in your model.
• E. Remove outlier data points to improve overall model performance.

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Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

• A. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• B. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
• C. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
• D. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
• E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

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