The frequency counting part is fine, everyone gets that.
Clarify the problem constraints (e.g., input size, definition of word, case sensitivity) and then propose a solution using a hash map to count frequencies, followed by sorting with a custom comparator that orders by frequency descending and lexicographically ascending for ties. Discuss time and space complexity, and consider alternative approaches like a heap for large k or when k is small relative to unique words.
Pro tip: Mention that you would use a min-heap of size k to achieve O(n log k) time, which is more efficient than sorting all unique words when k is much smaller than the number of unique words. Also, clarify edge cases like empty input, k larger than unique words, and how to handle case sensitivity.
Ask about input size, definition of a word, case sensitivity, and whether k can exceed the number of unique words. This ensures you handle edge cases correctly.
Use a hash map to count the frequency of each word in O(n) time. This is the standard first step for frequency-based problems.
Choose between sorting all unique words (O(m log m)) or using a min-heap of size k (O(m log k)), where m is the number of unique words. Discuss trade-offs based on constraints.
For sorting, use a comparator that sorts by frequency descending and alphabetically ascending for ties. For heap, define a custom comparator that keeps the k most frequent words with correct tie-breaking.
State time and space complexity, and walk through edge cases like empty input, k=0, k > unique words, and words with same frequency.
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