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

You are tasked with building a Snowpark application to process sensor data from IoT devices. The data arrives as JSON strings and needs to be transformed into a tabular format before being stored in a Snowflake table. You decide to use a User-Defined Table Function (UDTF) written in Python to handle this transformation. Which of the following approaches is the MOST efficient and scalable way to deploy and execute this UDTF in a production Snowpark environment, considering the possibility of high data volumes and concurrency?

• A. Package the Python UDTF into a zip file and upload it to a Snowflake stage. During UDTF registration, specify the stage location and use [...l, return_type=..., packages=['snowflake-snowpark-python', 'your_package'])'. Ensure that is appropriately tuned based on data size and complexity.
• B. Deploy the UDTF using the '@udtf decorator and explicitly specify a parameter when registering the function to control the size of input batches processed by each worker node.
• C. Deploy the UDTF as a stored procedure that is then called from your Snowpark application. The UDTF processes the data serially, one record at a time.
• D. Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
• E. Deploy the UDTF as an inline Python function within the Snowpark DataFrame transformation code, relying on the client-side Python interpreter for execution.

You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

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

Consider the following Snowpark code snippet that defines and registers a UDF:

Which of the following statements about this code are TRUE?

• A. The 'replace=True' argument ensures that any existing UDF with the same name ('ADD_SALUTATION') is overwritten.
• B. The 'input_types' parameter is redundant because Python's type hints are automatically used to determine the input types.
• C. The UDF is registered as a permanent UDF and stored in the specified stage for future use.
• D. The UDF is registered as a temporary UDF and will be removed when the session ends.
• E. The default value of 'salutation' in the Python function will be used even when calling the UDF from SQL if the salutation parameter is omitted.

You are tasked with optimizing a Snowpark application that processes sensor data'. The data includes timestamp, sensor ID, and sensor reading. Your initial implementation uses a regular Python UDF to calculate the moving average for each sensor. However, the processing time is significantly slow due to the large volume of data'. Which of the following strategies would be MOST effective in improving the performance of this calculation using vectorization?

• A. Rewrite the calculation logic using Snowpark's built-in aggregation functions instead of a UDF.
• B. Increase the warehouse size without modifying the UDF code.
• C. Replace the Python UDF with a SQL UDF as SQL UDFs are inherently faster.
• D. Convert the Python UDF to a Java UDF.
• E. Convert the existing Python UDF into a vectorized UDF using the '@vectorized' decorator, ensuring the input and output are Pandas Series.

You are migrating a Pandas-based data processing pipeline to Snowpark to leverage Snowflake's scalability and performance. One part of the pipeline involves a computationally intensive custom function that is applied row-by-row to a DataFrame using the 'apply' method in Pandas. When migrating this to Snowpark, what are the most effective strategies for achieving similar functionality while maximizing performance within the Snowflake environment?

• A. Directly translate the Pandas 'apply' operation to a Snowpark 'apply' operation, assuming that Snowpark's implementation is automatically optimized for distributed execution.
• B. Use a stored procedure to execute the pandas 'apply' row by row on the data from snowflake table.
• C. Create a Snowpark User-Defined Function (UDF) using Python and apply it to the DataFrame using the 'select method, leveraging Snowflake's distributed execution capabilities.
• D. Rewrite the custom function as a vectorized operation using Snowpark DataFrame functions and expressions, avoiding row-by-row processing.
• E. Utilize Snowpark's Pandas API to seamlessly execute the Pandas code within the Snowflake environment with minimal modifications.