You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

• A. Increase the number of UDF worker threads within the UDF definition.
• B. Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
• C. Convert the DataFrame to a Pandas DataFrame before applying the UDF.
• D. Broadcast the DataFrame to all compute nodes before applying the UDF.
• E. Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.

A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

• A. Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.
• B. Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety
• C. Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
• D. Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.
• E. Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.

A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

• A. Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.
• B. Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.
• C. Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.
• D. Implement a Streamlit application that calls the stored procedure when a button is pressed.
• E. Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.

Which of the following statements are correct regarding the authentication methods available when creating a Snowpark session?

• A. OAuth with external Oauth providers (like Okta or Azure AD) is not supported in snowpark, and only username password can be used.
• B. Key pair authentication uses a private key to establish a secure connection without needing to store passwords directly in the code.
• C. Using username/password authentication is the most secure and recommended approach for production environments.
• D. OAuth authentication requires pre-configuration in Snowflake and typically involves client IDs, client secrets, and refresh/access tokens.
• E. Snowflake supports MFA(Multi-Factor Authentication) using Web Browser Authentication, which needs to be enabled at an account level.

You have a Snowpark DataFrame named 'sales df containing sales data for different products. The DataFrame includes columns product_id' (INTEGER), 'sale_date' (DATE), 'quantity' (INTEGER), and 'price' (FLOAT). You need to calculate the total revenue for each product on a monthly basis and store the result in a new DataFrame named Which of the following Snowpark code snippets will correctly achieve this, while maximizing performance and minimizing data shuffling?

• A. ...python from snowflake.snowpark.functions import date_part, sum monthly_revenue_df = sales_df.groupBy('product_id', date_part('month', 'sale_date').alias('sale_month')).agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
• B. ...python from snowflake.snowpark.functions import date_format, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_format(sales_df['sale_date'], 'yyyy-MM')).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
• C. ...python from snowflake.snowpark.functions import monthname, sum monthly_revenue_df = sales_df.groupBy('product_id', monthname('sale_date').alias('sale_month')).agg(sum(sales_dfl'quantity'] sales_dfl'price']).alias('total_revenue'))
• D. ...python from snowflake.snowpark.functions import to_date, date_trunc, sum monthly_revenue_df = sales_df.withColumn('sale_month', date_trunc('MM', sales_df['sale_date'])).groupBy('product_id', 'sale_month').agg(sum(sales_df['quantity'] sales_df['price']).alias('total_revenue'))
• E. ...python from snowflake.snowpark.functions import month, sum monthly_revenue_df = sales_df.groupBy('product_id', month('sale_date').alias('sale_month')).agg(sum(sales_df['quantity'l sales_df['price']).alias('total_revenue'))