You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?

• A. Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:
  
• B. Robust scaling using interquartile range (IQR) in a stored procedure with Python:
  
• C. Min-Max scaling using the following SQL:
  
• D. Z-score standardization using the following SQL:
  
• E. Unit Vector normalization (L2 Normalization) using SQL:
  

You are a data scientist working for a retail company. You've been tasked with identifying fraudulent transactions. You have a Snowflake table named 'TRANSACTIONS' with columns 'TRANSACTION ID', 'AMOUNT', 'TRANSACTION DATE', 'CUSTOMER ID', and 'LOCATION'. You suspect outliers in transaction amounts might indicate fraud. Which of the following SQL queries is the MOST efficient and appropriate to identify potential outliers using the Interquartile Range (IQR) method, and incorporate necessary data type considerations for robust percentile calculations? Consider also the computational cost associated with each approach on a large dataset.

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

You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

• A. Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
• B. Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
• C. Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.
• D. Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
• E. Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).

You are evaluating a binary classification model built in Snowflake for predicting customer churn. You have access to the model's predictions on a holdout dataset, and you want to use both the ROC curve and the confusion matrix to comprehensively assess its performance. Which of the following statements regarding the interpretation and use of ROC curves and confusion matrices are correct in this scenario?

• A. The ROC curve visualizes the trade-off between true positive rate (sensitivity) and false negative rate (1 - specificity) at various threshold settings.
• B. The confusion matrix allows you to calculate precision, recall, F I-score, and accuracy, which are all useful for understanding the model's performance in terms of correctly and incorrectly classified instances.
• C. The area under the ROC curve (AUC) provides a single scalar value representing the overall discriminatory power of the model, with a higher AUC indicating better performance. An AUC of 0.5 indicates that the model performs no better than random chance.
• D. In Snowflake, you can generate ROC curves and confusion matrices directly using the 'SYSTEM$PREDICT function with appropriate parameters and visualizing the results using a tool like Snowsight or Tableau.
• E. While the ROC curve is independent of the class distribution, the metrics derived from the confusion matrix (e.g., precision, recall) can be significantly affected by imbalanced datasets.