I went straight for a sliding window with a hash set and it mostly worked, but I fumbled the part where you evict the element leaving the window.
Clarify the problem and edge cases, then propose an efficient sliding window solution using a hash set to track elements in the current window. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.
Pro tip: Mention that the sliding window with a hash set achieves O(n) time and O(N) space, which is optimal for this problem. Also, discuss how this approach can be extended to find the actual duplicate or handle streaming data.
Ask about input constraints (e.g., array size, integer range), window size N (e.g., N > array length), and expected output (boolean). Confirm whether N is always positive and less than or equal to array length.
Explain that you will maintain a window of size N and a hash set to track elements in the window. As you slide the window, add the new element and remove the old one, checking for duplicates.
Trace the algorithm on a small example to demonstrate correctness, showing how the set updates and when a duplicate is detected.
State that time complexity is O(n) since each element is added and removed at most once, and space complexity is O(N) for the hash set.
Mention that if N is small, a brute-force approach might be acceptable, but the sliding window is optimal. Also, note that if the array is sorted, a two-pointer approach could work, but sorting would add O(n log n) time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.