Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 2)

Question 1

You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

A.Minimize the amount of data transferred between the client and the engine by pushing down as much computation as possible to Snowflake using Snowpark DataFrame operations.

B.Utilize smaller batch sizes when writing data back to Snowflake to reduce memory pressure on the client.

C.Create and utilize temporary tables within Snowflake to store intermediate results of complex transformations.

D.Convert all dataframes to Pandas dataframes locally and perform data manipulation with Pandas methods to take advantage of local resources.

E.Increase the configuration parameter to maximize parallelism within the Snowpark engine without considering resources or potential bottleneck.

Question 2

You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A.Increase the warehouse size to the largest possible option.

B.Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.

C.Use the function to sort the data before performing the transformations.

D.Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.

E.Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.

Question 3

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 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.

B.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.

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 ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '

Question 4

A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

A.Generate an RSA key pair using 'ssh-keygen' and store the private key securely.

B.Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'snowflake'.

C.Set the 'AUTHENTICATOR parameter in the Snowflake CLI configuration to 'EXTERNALBROWSER.

D.Configure the user in Snowflake to use the public key by executing 'ALTER USER SET RSA_PUBLIC_KEY="'.

E.Set the 'PRIVATE KEY parameter in the Snowflake CLI configuration to the path of the private key file.

Question 5

A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?