Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then propose a solution that avoids full sorting, such as Quickselect (average O(n)) or a min-heap of size k (O(n log k)). Discuss trade-offs between time and space, and mention that Quickselect has worst-case O(n^2) but can be randomized for practical efficiency.
Pro tip: Mention that for ML engineering roles, you might need to handle streaming data or large datasets where a heap-based approach is more suitable, and always discuss the trade-offs between average and worst-case performance.
Ask about input size, value range, duplicates, and whether the array can be modified. Confirm if k is 1-indexed and within bounds.
Explain Quickselect: partition the array around a pivot and recursively search the side containing the kth largest. Alternatively, use a min-heap of size k.
State time and space complexity for each approach: Quickselect average O(n) time, O(1) space; heap O(n log k) time, O(k) space. Mention worst-case for Quickselect and how randomization mitigates it.
Compare approaches: Quickselect is faster on average but modifies input; heap is better for streaming or when k is small. Handle edge cases like k=1, k=n, empty array.
If asked, write clean code for one approach, e.g., Quickselect with random pivot, and test with examples.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.