Snowflake SPS-C01 Exam : Snowflake Certified SnowPro Specialty - Snowpark

Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

• A. Add 'session.close()' at the end of the main function.
• B. Add in sproc.register.
• C. Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
• D. Define 'session' as a global variable before calling 'session.sproc.register' .
• E. Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.

You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

• A. Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
• B. Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).
• C. Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
• D. Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
• E. Disable automatic query optimization features in Snowflake to gain more control over query execution.

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?

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

You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

• A. Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.
• B. Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
• C. Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
• D. Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.
• E. Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.

You have a Snowpark DataFrame 'df sales' containing sales data with columns like 'order id', 'product id', 'quantity', and 'sale_price' You want to persist this data into a Snowflake table named "SALES DATA'. You also want to create a dynamic table on top of this base table for faster analytics. You need to choose the appropriate persistence strategy and consider the implications of using a dynamic table. Which of the following options represents the BEST approach?

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