Start by clarifying the problem constraints (e.g., input size, k validity, tie-breaking) and then propose an efficient solution using a hash map to count frequencies followed by a heap or bucket sort to extract the top k. Discuss trade-offs between different approaches (e.g., sorting vs. heap vs. bucket sort) and analyze time/space complexity.
Pro tip: At Amazon, emphasize scalability and real-world applicability: mention that the heap approach is optimal for large streams or when k is small, and that bucket sort is O(n) when frequencies are bounded by n. Also, proactively discuss how you'd handle ties or if the input doesn't fit in memory.
Ask about input size, range of integers, whether k is always valid, and how to handle ties. Confirm expected output order (e.g., any order or sorted by frequency).
Propose at least two solutions: (1) hash map + sort, (2) hash map + min-heap of size k, (3) bucket sort. Compare time/space complexity and discuss which is best for given constraints.
Write clean code for the selected method, handling edge cases like k=0, k > unique elements, or empty array. Use appropriate data structures (e.g., Counter in Python, HashMap in Java).
State time and space complexity (e.g., O(n log k) for heap, O(n) for bucket sort). Walk through a small example and test edge cases.
Mention potential optimizations (e.g., quickselect for average O(n)) and how to handle streaming data or memory constraints. Be prepared for follow-up questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.