A financial institution wants to use Snowflake Cortex to analyze customer reviews and feedback extracted from various online sources to gauge customer sentiment towards their new mobile banking application. The goal is to identify positive, negative, and neutral sentiments, and also extract key phrases that drive these sentiments. Which of the following steps represent a viable workflow for achieving this using Snowflake Cortex and related functionalities?

• A. 1. Create a Streamlit application hosted externally that connects to the Snowflake database. 2. The Streamlit app uses a Python library like 'transformers' to perform sentiment analysis and key phrase extraction on the customer reviews read from Snowflake. 3. The results are then written back to a separate Snowflake table.
• B. 1. Ingest the customer reviews into a Snowflake table. 2. Use Snowflake's built-in 'NLP_SENTIMENT' function (if available) or a similar UDF based on a pre- trained sentiment analysis model to get the sentiment score. 3. Use regular expressions in SQL to extract key phrases based on frequency and context.
• C. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKE.ML.PREDICT' function with a sentiment analysis model to determine the overall sentiment score for each review. 3. Apply a separate key phrase extraction model via 'SNOWFLAKE.ML.PREDICT' to identify important keywords in the reviews.
• D. 1. Ingest the customer reviews into a Snowflake table. 2. Create a custom JavaScript UDF that calls the Snowflake Cortex 'COMPLETE' endpoint with a prompt that asks for both sentiment and key phrases. 3. Store the results in a new Snowflake table.
• E. 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKML.PREDICT' function with the appropriate task-specific model to determine the sentiment score for each review. 3. Further fine-tune the sentiment model with customer review data to improve the score and accuracy.

You are tasked with training a logistic regression model in Snowflake using Snowpark Python to predict customer churn. Your data is stored in a table named 'CUSTOMER DATA' with columns like 'CUSTOMER D', 'FEATURE 1', 'FEATURE 2', 'FEATURE 3', and 'CHURN FLAG' (boolean representing churn). You plan to use stratified k-fold cross-validation to ensure each fold has a representative proportion of churned and non-churned customers. Which of the following code snippets demonstrates the correct way to perform stratified k-fold cross-validation with Snowpark ML? (Assume 'snowpark_session' is a valid Snowpark session object).

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You have a structured dataset in Snowflake containing customer information and purchase history. You aim to build a multi-class classification model to predict customer churn, categorizing customers into 'Low Risk', 'Medium Risk', and 'High Risk' of churning. After training the model, you want to evaluate its performance. Which of the following metrics and evaluation techniques, when used together, provide the MOST comprehensive understanding of the model's performance across all churn risk categories, especially when dealing with potential class imbalance?

• A. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
• B. Area Under the ROC Curve (AUC-ROC) for each class (one-vs-rest approach), Precision-Recall Curve for each class, and Cumulative Accuracy Profile (CAP) curve.
• C. Overall Accuracy, Precision, Recall, F I-Score for each class, and Confusion Matrix.
• D. Only Overall Accuracy and a confusion Matrix.
• E. Log Loss (Cross-Entropy Loss), Gini Coefficient, and Kolmogorov-Smirnov (KS) statistic.

You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

• A. Reduce the value of 'MAX for the UDF to process smaller batches of data.
• B. Increase the warehouse size to provide more memory per node.
• C. Increase the value of 'MAX BATCH_ROWS' for the UDF to process larger batches of data at once.
• D. Partition the input data into smaller chunks using SQL queries and call the UDF on each partition separately.
• E. Implement lazy loading of the model within the UDF, ensuring it's only loaded once per warehouse node and reused across multiple invocations within that node.

A financial services company wants to predict loan defaults. They have a table 'LOAN APPLICATIONS' with columns 'application_id', applicant_income', 'applicant_age' , and 'loan_amount'. You need to create several derived features to improve model performance.
Which of the following derived features, when used in combination, would provide the MOST comprehensive view of an applicant's financial stability and ability to repay the loan? Select all that apply

• A. Calculated as 'applicant_age applicant_age'.
• B. Calculated as 'applicant_income I loan_amount'.
• C. Calculated as 'applicant_age / applicant_income'.
• D. Requires external data from a credit bureau to determine total debt, then calculated as 'total_debt / applicant_income' (Assume credit bureau integration is already in place)
• E. Calculated as 'loan_amount I applicant_age' .