Went with a frequency map first, which is the obvious part.
Start by clarifying the problem constraints (e.g., input size, value ranges, whether k is always valid) 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 elements. Discuss trade-offs between different approaches (e.g., sorting vs. heap vs. bucket sort) and analyze time and space complexity.
Pro tip: Mention that bucket sort can achieve O(n) time by using the frequency as an index, which is optimal when the frequency range is bounded by n. Also, be prepared to discuss how to handle ties or if the order of output matters.
Ask about input size, value ranges, whether k is guaranteed valid, and if the output order matters. This shows attention to detail and helps choose the right approach.
Use a hash map to count the frequency of each element in O(n) time. This is a standard first step for frequency-based problems.
Choose an efficient method: a min-heap of size k (O(n log k)), bucket sort (O(n)), or quickselect (average O(n)). Discuss trade-offs based on constraints.
State the time and space complexity of your chosen approach and consider edge cases like k=1, k=n, or all elements having the same frequency.
Write clean code, possibly with helper functions, and 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.