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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

A) Use 'CREATE OR REPLACE FUNCTION mydb.public.calculate_discount(order_total DOUBLE, customer_segment STRING, promo_code STRING) RETURNS DOUBLE LANGUAGE PYTHON ..: with the updated UDF definition.
B) Use ALTER FUNCTION mydb.public.calculate_discount ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '
C) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
D) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.
E) Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.

2. You are working with a Snowpark DataFrame containing website traffic data'. The DataFrame has columns like 'date' , 'page_url', and 'visit_count'. You need to calculate the cumulative sum of visit counts for each 'page_url' over time (i.e., ordered by 'date'). However, you only want to consider data from the last 30 days for each calculation. Which of the following Snowpark code snippets will correctly achieve this using window functions with a frame specification?

A)

B)

C)

D)

E)

3. You are developing a Snowpark application using Visual Studio Code and the Snowflake VS Code extension. You want to configure the extension to automatically detect and use a specific Anaconda environment for your Snowpark development. Assuming you have already created an Anaconda environment named 'snowpark_env', which configuration setting in the VS Code settings.json file would correctly specify the Python path for the Snowflake extension?

A) "snowflake.snowpark.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
B) "python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
C) "snowsql.pythonPath": "/path/to/anaconda3/envs/snowpark_env/bin/python"
D) "snowflake.python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
E) "python.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"

4. You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

A) Increase the warehouse size to the largest possible value before executing the stored procedure.
B) Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.
C) Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.
D) Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.
E) Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.

5. You are developing a Snowpark application to perform complex data transformations on a large dataset stored in Snowflake. You need to optimize the application's performance. Which of the following strategies are MOST effective for improving performance within Snowpark?

A) Using user-defined functions (UDFs) written in Python for all transformations, regardless of complexity.
B) Using the 'collect()' method frequently to retrieve small subsets of data to the client for processing.
C) Materializing intermediate DataFrames aggressively to reduce memory usage.
D) Leveraging Snowpark's optimized functions and operations whenever possible, even if it requires rewriting some Python code.
E) Taking advantage of Snowpark's lazy evaluation by chaining transformations together before triggering execution with an action like 'collect()' or 'write()'.

Solutions: