The 0-indexed part is where people slip up.
Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then present multiple solutions: sorting, min-heap, and Quickselect. Compare their time/space complexities and trade-offs, and implement the most efficient one (Quickselect with average O(n) time) while handling edge cases.
Pro tip: At Meta, interviewers value clean, bug-free code and the ability to discuss trade-offs. Always mention Quickselect's worst-case O(n^2) and how randomization or median-of-medians can mitigate it, showing depth beyond the average case.
Ask about input size, value range, duplicates, and whether the array can be modified. Confirm that k is 0-indexed and that k is valid (0 ≤ k < n).
Outline sorting (O(n log n)), min-heap of size k (O(n log k)), and Quickselect (average O(n)). Explain when each is appropriate based on constraints.
Select Quickselect for best average performance, or min-heap if k is small or the array is a stream. Justify based on time/space complexity and practical factors.
Write clean code for the chosen approach, handling edge cases like k=0, k=n-1, duplicates, and empty array. Use randomization in Quickselect to avoid worst-case.
Walk through test cases (e.g., [3,2,1,5,6,4], k=2) and verify correctness. State time and space complexity, and mention potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.