SOL-C01 Question #301: Real Exam Question with Answer & Explanation

Question

Within a Snowflake Notebook, you have a Python script that performs several data transformations using Pandas DataFrames and then attempts to load the transformed data into a new Snowflake table. The script fails intermittently with 'MemoryError'. Which of the following strategies could you employ to mitigate the 'MemoryError' when working with Snowflake Notebooks and Python? (Choose all that apply.)

Options

• AIncrease the virtual warehouse size allocated to the Snowflake session.
• BImplement chunking: Read the data from Snowflake in smaller batches, process each batch
• COptimize the Pandas DataFrame's data types to use less memory (e.g., using 'int32 instead of
• DUtilize the `COPY INTO' command directly from Snowflake to load data into the table, bypassing
• EReduce the number of concurrent Snowflake Notebook sessions running.

Explanation

Options B, C, and D are correct. A MemoryError indicates that the Python process within the Snowflake Notebook is running out of memory. Chunking (B) allows processing of data in smaller, manageable pieces. Optimizing Pandas DataFrame data types (C) reduces the memory footprint of each DataFrame. Using 'COPY INTO' (D) leverages Snowflake's internal data loading capabilities, avoiding the need to load the entire dataset into a Pandas DataFrame. Increasing the virtual warehouse size (A) primarily affects query performance within Snowflake, not the memory available to the Python process in the Notebook. Reducing the number of concurrent sessions might help if resource contention is the root cause, but is less directly related to the error, so E is not the strongest solution.

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