SPS-C01 Exam Dumps | Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values,

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Question 149/151

Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A. The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.

B. Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.

C. The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.

D. Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.

E. Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.

Correct Answer: C

The method, when used with the default 'block=True' , operates synchronously, meaning it waits for the result to be fully available before proceeding to the next line of code. This can introduce latency and slow down the application, especially with larger datasets. Using 'block=False' makes the operation asynchronous, allowing the application to continue executing other tasks while the data is being retrieved in the background. Increasing warehouse size will have the same effect regardless, because of . The 'block' parameter is crucial for controlling synchronous versus asynchronous behaviour. The warehouse has no direct influence since the bottleneck is with 'collect()'. Converting to pandas does not fix the inherent blocking problem and makes the problem worse, given it increase network I/O to move data to client side.

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