It's basically the classic top-K frequency problem but with strings.
Start by clarifying the problem constraints (e.g., input size, definition of 'top', tie-breaking). Then propose a two-step solution: count frequencies with a hash map, and use a min-heap of size K to extract the top K elements, achieving O(n log k) time. Discuss trade-offs and edge cases before coding.
Pro tip: Mention that using a min-heap of size K is optimal for large n and small K, and that tie-breaking should be defined (e.g., lexicographical order) to avoid ambiguity. Also, note that if K is close to the number of unique strings, a full sort might be simpler and equally efficient.
Ask about input size, definition of 'top' (e.g., highest frequency), tie-breaking rules, and whether K can be larger than the number of unique strings. This ensures you solve the correct problem.
Iterate through the list and build a hash map (dictionary) mapping each string to its frequency. This takes O(n) time and O(n) space.
Iterate over the frequency map and maintain a min-heap of size K based on frequency. For each element, if the heap size is less than K, push it; else if its frequency is greater than the heap's minimum, pop and push. This yields O(n log k) time.
After processing all elements, the heap contains the top K strings. Pop them into a list and reverse it to get descending order (or sort if tie-breaking requires).
State time complexity O(n log k) and space O(n + k). Discuss edge cases: empty input, K=0, K > unique count, and ties. Optionally, mention alternative approaches like bucket sort for O(n) when frequencies are bounded.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.