The no-most_common constraint is what trips people up here.
Start by clarifying the problem constraints (e.g., input size, uniqueness of answer, k validity). Then propose a two-step solution: first compute frequencies using a hash map, then select the top k using a min-heap of size k or bucket sort for O(n) time. Discuss trade-offs between approaches and analyze time/space complexity.
Pro tip: Mention that you would avoid sorting all unique elements (O(n log n)) by using a heap or bucket sort, and explicitly state that you're not using built-in frequency shortcuts like collections.Counter. This shows awareness of both efficiency and the constraint.
Ask about input size, range of integers, whether k is always valid, and if the answer order matters. Confirm that built-in frequency shortcuts are disallowed.
Use a hash map (dictionary) to count occurrences of each element by iterating through the array. This is O(n) time and O(n) space.
Choose an efficient selection method: either use a min-heap of size k (O(n log k)) or bucket sort by frequency (O(n)). Explain why this avoids full sorting.
State time and space complexity for your chosen approach. Discuss edge cases like k=1, k=number of unique elements, or empty array.
Walk through a small example to verify correctness, such as array [1,1,1,2,2,3] and k=2, showing the frequency map and final output.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.