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You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very low latency (under 100ms) to prevent fraudulent transactions. The input data is streamed into a Snowflake table. You are considering using either a Scalar or Vectorized Python UDF for scoring. Which of the following approaches and considerations are MOST critical for achieving the desired performance and reliability? Assume the model itself is computationally inexpensive. Select all that apply.

• A. Configure Snowflake's Auto-Suspend feature to aggressively suspend the warehouse when idle, to minimize costs.
• B. Use a Scalar UDF because it has lower overhead per invocation compared to a Vectorized UDF when processing individual transactions.
• C. Utilize Snowflake's Materialized Views to pre-compute frequently used features, reducing the amount of data the UDF needs to process.
• D. Use a Vectorized UDF with a small 'MAX BATCH_SIZE to minimize latency while still leveraging vectorization benefits.
• E. Pre-load the model into a static variable within the UDF code, ensuring it's only loaded once per worker node.

You are developing a machine learning model using scikit-learn within Visual Studio Code (VS Code) and connecting directly to Snowflake to access a large dataset. You need to authenticate to Snowflake using Key Pair Authentication, but want to avoid storing the private key directly within your VS Code project or environment variables for security reasons. Which of the following approaches offers the MOST secure way to manage and access the private key for Snowflake authentication from VS Code?

• A. Store the encrypted private key in a configuration file within your VS Code project and decrypt it at runtime using a password-based encryption algorithm.
• B. Store the private key in a secure vault (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) and retrieve it dynamically within your VS Code script using the appropriate API or SDK.
• C. Store the private key in a password-protected ZIP archive and extract it during the Snowflake connection process.
• D. Use the Snowflake CLI to generate a temporary access token and hardcode it into your VS Code script for authentication.
• E. Store the private key in a secure database table within Snowflake and query it dynamically.

You have deployed a custom model using Snowpark within Snowflake. The model is designed to predict customer churn, and you've wrapped it in a User-Defined Function (UDF) for easy use. The UDF takes several customer features as input and returns a churn probability. However, you notice the UDF's performance is slow, especially when scoring large batches of customers. Which of the following strategies would be most effective in optimizing the performance of your model deployment within Snowflake? Assume the UDF is already using vectorization techniques.

• A. Implement row-level security on the input data. This enhances security and implicitly improves query performance because the model only processes authorized data.
• B. Increase the warehouse size used by Snowflake. This provides more resources for the UDF execution.
• C. Utilize a vectorized UDF that can process multiple rows in a single call, further leveraging Snowflake's parallel processing capabilities. Ensure it supports the correct data types for both input and output. Consider using a Pandas UDF if Python is the underlying language.
• D. Re-write the UDF in SQL instead of Snowpark to avoid the overhead of the Snowpark API.
• E. Cache the results of the UDF using Snowflake's result caching feature. This will avoid re-executing the UDF for the same input values.

You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

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. Overall Accuracy, Precision, Recall, F I-Score for each class, and Confusion Matrix.
• B. Only Overall Accuracy and a confusion Matrix.
• C. Log Loss (Cross-Entropy Loss), Gini Coefficient, and Kolmogorov-Smirnov (KS) statistic.
• D. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
• E. 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.