DSA-C03 Exam Dumps | You are tasked with building a machine learning model in Python using data stored in Snowflake. You need

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Question 113/143

You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.

B. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.

C. Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.

D. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.

E. Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.

Correct Answer: C

Option C is the most efficient. 'execute_stream' allows you to fetch data in chunks, preventing out-of-memory errors with large tables. You can perform transformations on each chunk, reducing the memory footprint. Loading the entire table at once (A) is inefficient for large datasets. Using ssnowsqr (B) or 'COPY INTO' (E) adds an extra step of unloading and reloading, increasing the time taken. Creating a Snowflake view (D) is a good approach for pre-processing but might not fully address memory issues during the final load into Pandas, especially if the view still contains a large amount of data.

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