Snowflake SPS-C01 valid exam dumps : Snowflake Certified SnowPro Specialty - Snowpark

You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

• A. Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
• B. Create a UDF and grant USAGE on the stage to the role that owns the UDF.
• C. Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
• D. Create an external function and grant access to the API integration that provides the security context.
• E. Create a secure UDF and use the function to access stage credentials within the UDF's handler function.

A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?

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You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?

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You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

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You are developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations including filtering, aggregation, and joining with another DataFrame, and then writes the results back to a new table. You want to optimize the execution plan to minimize data movement and processing time. Which of the following strategies would be MOST effective in leveraging Snowpark's lazy evaluation capabilities to achieve this optimization?

• A. Defining all transformations in a single, complex SQL query string and using to execute it.
• B. Calling 'cache()' on the initial DataFrame read from the table to materialize it in memory before any transformations.
• C. Chaining all the transformations together using DataFrame methods (e.g., 'filter()' , 'groupBy()' , 'join()') and only calling or at the very end.
• D. Calling after each transformation to materialize intermediate results and then creating new DataFrames for subsequent operations.
• E. Executing each transformation in separate Python processes using multiprocessing to parallelize the workload.