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I jumped straight to sorting by frequency, which works but is O(n log n).
Start by clarifying the problem constraints (e.g., array size, value range, memory limits) and then propose a solution using a hash map to count frequencies, followed by a heap or bucket sort to extract the top K elements. Discuss trade-offs between different approaches (e.g., sorting vs. heap vs. bucket sort) and analyze time/space complexity.
Pro tip: Mention that you would handle ties consistently (e.g., by any order) and consider edge cases like K larger than the number of unique elements or empty input. Also, briefly discuss how the solution could be adapted for streaming data or distributed systems, showing awareness of real-world scalability.
Ask about input size, value range, memory limits, and whether the array can be modified. Confirm that ties can be broken arbitrarily and that K is positive and not larger than the number of unique elements.
Use a hash map to count the frequency of each element. This takes O(n) time and O(n) space in the worst case.
Choose an efficient method: (a) min-heap of size K for O(n log K) time, (b) bucket sort for O(n) time when frequencies are bounded by n, or (c) quickselect for average O(n) time. Explain the trade-offs.
State the time and space complexity of your chosen approach. Discuss edge cases: empty array, K=0, K > unique elements, all elements same, etc.
If needed, suggest optimizations for large-scale or streaming data, such as using a distributed hash map or approximate algorithms. Mention that the solution can be adapted to return elements in any order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.