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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with optimizing the performance of a Snowpark application that uses a UDF to perform complex image processing. The UDF is currently registered using 'session.udf.registeff. You observe that the UDF execution is slow, particularly when processing large batches of images. What steps could you take to potentially improve the performance of this UDF execution? Select all that apply.

A) Ensure that the stage location specified during UDF registration is in the same region as the Snowflake account to minimize data transfer latency.
B) Convert the UDF into a UDTF (User-Defined Table Function) if the image processing can benefit from row-by-row processing and aggregation.
C) Increase the warehouse size used for running the Snowpark application to provide more computational resources for the UDF execution.
D) Ensure that the size of the zipped file containing the UDF's dependencies is as small as possible to speed up deployment.
E) Use session.clear_packages() to clear the session packages when deploying UDF.

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

3. You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:

A)

B)

C)

D)

E)

4. You are working with semi-structured data in Snowflake stored in a VARIANT column named 'payload'. You want to extract specific fields from this VARIANT column within a SQL query used to create a Snowpark DataFrame. Which of the following approaches allows you to access nested fields within the 'payload' column directly in the SQL query and create a corresponding column in your Snowpark DataFrame? Select all that apply.

A) Use the "LATERAL FLATTEN(input payload)' function within the SQL query to unnest the VARIANT and then access the fields.
B) First create a temporary table containing only the extracted fields using a separate SQL query, then create a Snowpark DataFrame from that table.
C) Extract the VARIANT data into a Pandas DataFrame and then use Pandas to access the nested fields before creating the Snowpark DataFrame.
D) Use the 'fieldl .field2')' function within the SELECT statement in the SQL query.
E) Use the 'payload:fieldl :field2 syntax directly within the SELECT statement in the SQL query.

5. A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A)

B)

C)

D)

E)

Solutions: