Start by clarifying the problem constraints (e.g., input size, uniqueness of answer, time/space limits). Then propose an efficient solution using a hash map to count frequencies and a heap or bucket sort to select the top k elements. Discuss trade-offs between different approaches and analyze time/space complexity.
Pro tip: Mention that bucket sort can achieve O(n) time when frequencies are bounded by n, which is optimal for large datasets. Also, proactively discuss how to handle ties or if the answer order doesn't matter, showing attention to edge cases.
Ask about input size, range of values, whether k is always valid, and if the order of output matters. Confirm expected time/space complexity.
Propose using a hash map to count frequencies, then sort the unique elements by frequency and take the top k. Mention this is O(n log n) time.
Explain that a min-heap of size k can achieve O(n log k) time, or bucket sort can achieve O(n) time by using frequency as index. Discuss trade-offs.
State time and space complexity for the chosen approach. Discuss edge cases like k=1, all elements same frequency, or k equal to number of unique elements.
Write clean code with meaningful variable names. Walk through a small example to verify correctness and handle any off-by-one errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.