Got the naive approach out fast, count frequencies then sort by count, easy enough.
Start by clarifying the problem constraints and edge cases, then present a straightforward solution using a frequency map and sorting. Next, optimize by using a min-heap of size k to track the top k frequent elements, explaining the time and space complexity trade-offs.
Pro tip: Demonstrate awareness of real-world constraints: discuss how the choice between sorting and heap depends on the relative sizes of k and the number of distinct elements, and mention that for very large datasets, a distributed approach might be needed.
Ask about input size, whether k is always valid, if the order of output matters, and if there are memory constraints. This shows attention to detail and helps tailor the solution.
Build a frequency map using a hash map, then sort the distinct elements by frequency and return the top k. Analyze time complexity: O(n + d log d) where d is number of distinct elements.
Instead of sorting all distinct elements, use a min-heap of size k to keep track of the k most frequent elements. Iterate through the frequency map, push each element onto the heap, and if size exceeds k, pop the smallest frequency. Finally, extract elements from the heap.
Compare the optimized solution's time complexity O(n + d log k) and space O(d + k) with the basic solution. Discuss when each approach is preferable, e.g., if k is much smaller than d, heap is better; if k is close to d, sorting might be simpler.
Consider edge cases: k=0, k greater than number of distinct elements, all elements same, negative numbers, etc. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.