You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

• A. Combine the PDP for 'location' with a two-way PDP showing the interaction between 'location' and 'square_footage'.
• B. Generate ICE (Individual Conditional Expectation) plots alongside the PDP to assess the heterogeneity of the relationship between 'location' and predicted price.
• C. Use one-hot encoding for the 'location' feature and generate individual PDPs for each one-hot encoded column.
• D. Replace the 'location' feature with a numerical feature representing the average house price in each neighborhood, calculated from historical data.
• E. Remove the 'location' feature from the model, as categorical features are inherently difficult to interpret.

A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

• A. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
• B. The CLT is applicable, and the sample mean will converge to the population median.
• C. The CLT is not applicable because the population distribution (call durations) is heavily skewed.
• D. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
• E. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.

You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

• A. Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
• B. Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
• C. Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
• D. Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
• E. Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.