Start by clarifying the problem constraints (e.g., array size, value range, expected time/space complexity). Then propose an efficient 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.
Pro tip: Mention edge cases like k > number of unique elements or negative numbers, and discuss how to handle them. Also, if the interviewer allows, suggest a quickselect-based approach for O(n) average time, showing depth.
Ask about input size, value range, whether k is always valid, and if the output order matters. This shows attention to detail and helps choose the right algorithm.
Use a hash map to count occurrences of each element. This takes O(n) time and O(n) space.
Use a min-heap of size k to keep the k most frequent elements, or bucket sort if frequencies are bounded. Discuss time and space complexity.
Address cases like k=0, k > unique elements, or all elements same. Mention alternative approaches like quickselect for O(n) average time.
Compare heap vs bucket sort vs quickselect in terms of time/space and when to use each. This demonstrates algorithmic maturity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.