My first instinct was to brute force it with two nested loops and I actually started explaining that before catching myself.
Start by clarifying the problem: define what happens at the boundaries (index 0 and n-1) and whether the pivot index itself is excluded from both sums. Then propose an O(n) time, O(1) space solution using a running total: compute the total sum, iterate while maintaining left sum, and check if left sum equals total - left sum - current element. If asked for code, write clean, bug-free code and test with edge cases like empty array, single element, and no valid index.
Pro tip: Before coding, explicitly state the time and space complexity of your approach and compare it to the brute-force O(n^2) method. This shows you think about efficiency and trade-offs, which is highly valued at Meta.
Ask clarifying questions: What should be returned if no such index exists? Is the pivot index included in either sum? How to handle empty array or multiple valid indices?
Mention the naive O(n^2) solution of checking each index by summing left and right sides. Then propose the optimal O(n) time, O(1) space solution using prefix sums.
Compute the total sum of the array. Iterate through the array while maintaining a running left sum. At each index i, check if left sum equals total - left sum - nums[i]. If true, return i; otherwise, add nums[i] to left sum.
Write clean code in your preferred language, handling edge cases such as empty array (return -1) and single element (return 0). Use meaningful variable names and add comments.
Walk through test cases: [1,7,3,6,5,6] returns 3; [1,2,3] returns -1; [2,1,-1] returns 0. State time complexity O(n) and space complexity O(1).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.