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You are using the NetworkX library in Snowpark Python to analyze social network data stored in a Snowflake table named 'USER CONNECTIONS', which has columns 'USER ID' and 'CONNECTED USER representing connections between users. You want to find the users with the highest 'betweenness centrality' to identify influential nodes in the network. Which Snowpark Python code snippet would correctly calculate and display the top 5 users with the highest betweenness centrality?
Correct Answer: A
Option A is the most efficient and correct approach. It leverages for directly creating the graph from a Pandas DataFrame (converted from the Snowpark DataFrame), calculates betweenness centrality using NetworkX, creates a Pandas DataFrame for results, and then sorts and displays the top 5 users. Options B iterates through the rows, which is less efficient, and attempts to create a Snowpark DataFrame from the betweenness dictionary, which isn't the most efficient output mechanism in this context. Option C is almost correct but uses 'nlargest' which is also valid. Option D uses which is slower and less efficient than Option E is very close but includes the parameter which is unnecessary for this specific operation since it's the initial creation of the betweenness df and the index isn't crucial to be reset. So while it functions it is redundant.