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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

A Snowpark application needs to process a large dataset (1 TB) residing in Snowflake, performing complex transformations. Due to network constraints and the complexity of the transformations, the execution takes a considerable amount of time. The application's end-users are complaining about the latency. Which of the following strategies would MOST effectively enhance performance, specifically targeting the reduction of overall execution time considering the synchronous vs. asynchronous execution models and the implications of the 'block' parameter?

A. Increase the Snowflake warehouse size to improve compute capacity, regardless of the synchronous or asynchronous execution model.
B. Employ asynchronous actions with 'block=True' to immediately retrieve results, ensuring data consistency but potentially increasing overall latency due to blocking.
C. Utilize synchronous actions to trigger transformations and retrieve results; this approach guarantees immediate availability but might not be optimal for long-running, complex operations.
D. Minimize data transfer by applying aggressive filtering and aggregation within Snowflake before transferring data to the Snowpark application for further processing, and use synchronous action with a small warehouse size.
E. Implement asynchronous actions with 'block=False' to initiate transformations without waiting for completion, allowing other parts of the application to proceed concurrently. Subsequently, use a mechanism (e.g., polling, callbacks) to retrieve the results when available, leveraging parallel execution to enhance overall responsiveness.

Question 2

You have a CSV file stored in a Snowflake stage named 'my_stage/data.csv'. The file contains customer data, including 'customer id' (INT), 'first_name' (VARCHAR), 'last_name' (VARCHAR), and 'email' (VARCHAR). You want to create a Snowpark DataFrame representing this data, explicitly defining the schema for improved type safety and performance. Which of the following code snippets is the MOST efficient and correct way to create the DataFrame with the specified schema, assuming you have a valid Snowpark session object named 'session'?

A.

B.

C.

D.

E.

Question 3

You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

A. No special steps are required when creating the Snowpark session; the UDF automatically inherits the caller's privileges.
B. When defining the UDF using Snowpark, ensure the argument is passed in the decorator. Create the Snowpark session as usual.
C. When creating the Snowpark session, explicitly set the 'privilege' parameter to 'CALLER.
D. The account administrator needs to explicitly grant the 'CREATE FUNCTION' privilege to the user.
E. After creating the Snowpark session, execute the SQL command 'ALTER SESSION SET

Question 4

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. Define 'session' as a global variable before calling 'session.sproc.register' .
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. Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.

Question 5

You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:

A.

B.

C.

D.

E.

Solutions: