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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Which of the following statements are correct regarding account identifiers and their usage when creating Snowpark sessions in Python?

A) Account identifiers are case-sensitive and must be entered exactly as provided by Snowflake.
B) If the account identifier includes the region ID, you do not need to specify the region separately in the connection parameters.
C) The account identifier can be specified using either the 'Organization Name-Account Name' format or the legacy account locator, depending on the Snowflake account configuration and the region.
D) Using the 'Organization Name-Account Name' format for the account identifier is only valid for accounts that have been recently created.
E) Account identifiers can only be used when connecting to Snowflake accounts in the same AWS region.

2. A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?

A)

B)

C)

D)

E)

3. You are developing a Snowpark Python application to process large datasets stored in a Snowflake table called 'CUSTOMER DATA' The application needs to perform complex data transformations and aggregations that benefit from Snowpark's lazy evaluation and query optimization. Which of the following approaches will lead to the MOST efficient execution in terms of resource utilization and performance?

A) Execute a series of individual SQL queries using 'session.sql()' to perform the transformations and aggregations, and then combine the results into a final Pandas DataFrame for further processing before loading it back into Snowflake.
B) Fetch all the data from 'CUSTOMER_DATR into a Pandas DataFrame using , perform the transformations in Pandas, and then load the results back into a new Snowflake table using 'session.write_pandas()'.
C) Load the data into a temporary table, then create a Stored Procedure in SQL to perform transformations, and call the procedure from the Snowpark Python application.
D) Create a Snowpark DataFrame from the "CUSTOMER_DATX table using 'session.table('CUSTOMER DATA')', perform all the transformations and aggregations using Snowpark DataFrame API, and materialize the final result into a new Snowflake table using .
E) Use 'session.table('CUSTOMER to fetch all the data into a list of Row objects in Python, perform transformations using standard Python loops and data structures, and then write the processed data to a new Snowflake table using 'session.createDataFrame(V.

4. Consider the following Python code snippet using Snowpark:

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

A) The code reads data from a table named 'my_table' in Snowflake.
B) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
C) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
D) 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.
E) The code establishes a connection to Snowflake using the provided credentials.

5. 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) Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.
B) Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.
C) Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.
D) Increase the warehouse size to the largest possible value before executing the stored procedure.
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.

Solutions: