Exam SPS-C01 Topic 1 Question 234 Discussion

Actual exam question for Snowflake's SPS-C01 exam
Question #: 234
Topic #: 1

You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A. Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation. B. Using without specifying a storage level. Snowflake will choose a default storage level. C. Using immediately after the initial DataFrame creation. D. Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation. E. Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.

Suggested Answer: C Vote an answer

' df.cache_result()' is the recommended approach for caching DataFrames in Snowpark. It is designed to efficiently materialize the results of a DataFrame and store them in Snowflake's internal cache. This avoids recomputation in subsequent operations. brings the entire DataFrame into the client's memory, which is inefficient for large datasets. is not directly available in Snowpark like Spark. Creating a temporary table involves unnecessary I/O operations. Converting to Pandas and back introduces overhead and defeats the purpose of using Snowpark's optimized execution.

by Walter at Feb 23, 2026, 12:08 AM

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