My first instinct was to sort the whole list and slice the top k, which works but I kept second-guessing myself on whether they wanted a heap-based solution for efficiency.
Clarify that since probability is linearly tied to spend, ranking by spend is equivalent to ranking by probability. Then, use a min-heap of size k to efficiently find the top k participants in O(n log k) time, or a selection algorithm like Quickselect for O(n) average time. Discuss trade-offs and handle edge cases.
Pro tip: Mention that if the input is already sorted or if k is small relative to n, a heap is optimal; but if k is close to n, sorting might be simpler. Also, consider that probability might need normalization, but for ranking it's irrelevant.
Confirm that probability is directly proportional to spend, so ranking by spend is sufficient. Ask about input size, whether k is guaranteed valid, and if there are ties.
Decide between sorting (O(n log n)), min-heap (O(n log k)), or Quickselect (O(n) average). Consider constraints and trade-offs.
Write code for the chosen approach, ensuring correct handling of edge cases like k=0, k>n, or empty list.
State time and space complexity, and explain why the chosen method is efficient for the given constraints.
Walk through examples, including edge cases, to verify correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.