A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

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

You have created a JavaScript UDF named 'calculate discount' in Snowflake that takes two arguments: 'product_price' (NUMBER) and 'discount_percentage' (NUMBER). The UDF calculates the discounted price using the formula: 'product_price (1 - discount_percentage / 100)'. However, when you call the UDF with certain input values, you are encountering unexpected results, specifically with very large or very small numbers due to JavaScript's number precision limitations. Which of the following strategies can you implement to mitigate this issue and ensure accurate calculations within your JavaScript UDF?

• A. Use JavaScript's 'toFixed(V method to round the result to a fixed number of decimal places.
• B. Avoid large or small number and stick to the limited range of input values.
• C. Cast input arguments and the result to 'FLOAT within the UDF.
• D. Utilize a JavaScript library specifically designed for handling arbitrary-precision arithmetic, such as 'Big.js' or 'Decimal.jS , within the UDF.
• E. Convert the input numbers to strings within the JavaScript UDF before performing the calculation.

A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

• A. Implement data clustering on a column that is frequently used in time travel query filters.
• B. Decrease the parameter for the 'SALES_DATA' table.
• C. Periodically clone the ' SALES DATA' table to a separate historical table.
• D. Increase the parameter for the 'SALES_DATX table.
• E. Create a materialized view that pre-computes the results of common time travel queries.

You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?

• A. A Python UDTF using Snowpark, leveraging external libraries like 'torch' for accelerated calculations and ML model inference by GPU.
• B. A JavaScript UDF that uses basic JavaScript functions to perform the entire scoring algorithm without external dependencies.
• C. A Java UDTF that leverages a custom Java library for feature engineering and model prediction, deployed as a JAR file to Snowflake's internal stage.
• D. A Python UDF that loads a pre-trained machine learning model (e.g., using scikit-learn) and performs predictions on the input data.
• E. A SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.