You are developing a model to predict house prices based on structured data including size, number of bedrooms, location, and age. You have built a linear regression model within Snowflake. During the evaluation, you observe that the residuals exhibit heteroscedasticity. Which of the following actions is the LEAST appropriate to address heteroscedasticity in this scenario, considering you want to implement the solution primarily using Snowflake's built-in features and capabilities?

• A. Implement Weighted Least Squares (WLS) regression by calculating weights inversely proportional to the variance of the residuals for each data point. This involves creating a UDF to calculate weights and modifying the linear regression model fitting process. (Assume direct modification of the fitting process is possible within Snowflake).
• B. Use robust standard errors in the linear regression analysis, even though Snowflake doesn't directly support calculating them. You decide to export model coefficients to an external statistics package (e.g., Python with Statsmodels) to compute robust standard errors and then bring insights back to Snowflake.
• C. Include interaction terms between the independent variables in your linear regression model.
• D. Transform independent variables using Box-Cox transformation and include in Snowflake Linear Regression Model Training
• E. Apply a logarithmic transformation to the target variable ('SALES_PRICE) using the 'LOG' function within Snowflake before training the linear regression model.

A marketing analyst at 'NovaRetail' suspects that a new advertising campaign has increased the average purchase amount. They have historical purchase data in a Snowflake table called 'purchase_historf. To validate their hypothesis using the Central Limit Theorem (CLT), they perform the following steps: 1. Calculate the population mean (?) of purchase amounts from the historical data'. 2. Draw 500 random samples of size 50 from the table. 3. Calculate the sample mean (x?) for each sample. Which of the following steps are essential for correctly applying the Central Limit Theorem to perform a z-test to determine whether the new advertising campaign has significantly increased the average purchase amount?

• A. Ensure that the samples are drawn independently and randomly.
• B. Calculate the standard deviation of the population (?) from the historical data and estimate the standard error of the mean as ? / sqrt(50).
• C. Verify that the sample size (n=50) is sufficiently large to approximate normality of the sample mean distribution based on the CLT. This implicitly assumes population size is significantly larger than the sample size.
• D. Calculate the standard deviation of the sample means and use it as an estimate for the standard error of the mean.
• E. Check if the original population distribution (purchase amounts) is approximately normally distributed.

A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

• A. Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.
• B. Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
• C. Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
• D. Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
• E. Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.

A data scientist is tasked with building a real-time customer support system using Snowflake Cortex. The system needs to analyze incoming customer messages and categorize them into predefined issue types (e.g., billing, technical support, account management) for efficient routing to the appropriate support team. Considering the need for low latency and high accuracy, which of the following approaches would be the MOST suitable for implementing this categorization task using Snowflake Cortex, considering the costs and trade-offs involved?

• A. Creating a series of SQL 'CASE' statements to categorize the messages based on keyword matching within the message text. Use regular expressions for more complex pattern matching.
• B. Developing a custom Python UDF that uses a third-party LLM API (e.g., OpenAl) to categorize the messages and deploying it in Snowflake, handling API authentication and rate limiting within the UDF.
• C. Fine-tuning a pre-trained language model within Snowflake using the 'CREATE SNOWFLAKE.ML.ANACONDA_MODEL' command on a dataset of historical customer messages and their corresponding issue types, then deploying this fine-tuned model for real-time categorization via a user-defined function (UDF).
• D. Directly calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt for each incoming message, instructing it to categorize the message based on the predefined issue types.
• E. Leveraging the Snowflake Cortex built-in categorization task-specific model (e.g., using the 'SNOWFLAKE.ML.PREDICT' function with the appropriate model name) to categorize incoming messages without any fine-tuning.