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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A) Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.
B) Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.
C) Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.
D) Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
E) Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.

3. Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
B) The code reads data from a table named 'my_table' in Snowflake.
C) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
D) The code establishes a connection to Snowflake using the provided credentials.
E) The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.

4. You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A) Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
B) Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.
C) Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
D) Increase the warehouse size used for the Snowpark session to provide more computational resources.
E) Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.

5. You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.

A) Rewrite the UDF in Java using Snowpark API, which generally has a smaller memory footprint than Python. Deploy the UDF with the same warehouse size.
B) Implement a caching mechanism within the UDF to store intermediate results and reduce the number of calls to the external library. Deploy the UDF with the same warehouse size.
C) Modify the UDF to process data in smaller batches using a generator pattern, reducing memory consumption at any given time. Deploy the UDF with the same warehouse size.
D) Increase the warehouse size to the largest available option. This will provide more memory to the UDE
E) Implement the Python UDF as a Snowpark Stored Procedure. Deploy the UDF with the same warehouse size.

Solutions: