Utilize the 'explode' function to generate a new row for each element in the array, enabling individual analysis of each element. This method leverages built-in Spark functionality for optimal performance and scalability.

Implement the 'flatten' function to merge all elements into a single string, simplifying the dataset for bulk operations. While this reduces row count, it compromises the ability to analyze individual elements directly.

Develop a custom UDF (User Defined Function) to pre-filter elements based on specific conditions before applying the 'explode' function. This offers tailored flexibility but at the cost of increased complexity and potential performance drawbacks.

First apply 'flatten' to condense the array into a single string, then use 'explode' to separate each character for analysis. This approach is inefficient and unsuitable for analyzing individual integer elements as required.

Both 'A' and 'C' are viable under different circumstances. 'A' is preferable for general use due to its simplicity and performance, whereas 'C' is better suited for scenarios requiring preliminary filtering.