Start by clarifying the problem constraints: are the queries frequent and updates rare or nonexistent? If the array is static, precompute a prefix sum array to answer each query in O(1) time. If updates are allowed, discuss trade-offs between a simple array (O(n) query) and a Fenwick tree or segment tree (O(log n) query and update).
Pro tip: Meta interviewers value clean, bug-free code and the ability to discuss trade-offs. After implementing the prefix sum solution, mention that it's optimal for static arrays but if updates are needed, a Fenwick tree would be more suitable, showing you think beyond the immediate problem.
Ask whether the array is static or if updates are allowed, and the expected frequency of queries. This determines the optimal data structure.
For a static array, suggest precomputing a prefix sum array where prefix[i] = sum of elements from index 0 to i-1. Then range sum from i to j is prefix[j+1] - prefix[i].
Preprocessing takes O(n) time and O(n) space. Each query is O(1). If updates are allowed, discuss alternatives like Fenwick tree with O(log n) per operation.
Write clean code for the prefix sum approach, handling edge cases like empty array or invalid indices. If updates are needed, implement a Fenwick tree.
Walk through examples, test edge cases, and compare with other data structures (e.g., segment tree) in terms of time and space complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.