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Start by clarifying requirements such as case sensitivity, punctuation handling, and what constitutes a word. Then propose a two-pass approach: first count word frequencies using a hash map, then use a min-heap of size N to efficiently extract the top N words. Discuss trade-offs between time and space complexity, and mention edge cases like ties or fewer than N unique words.
Pro tip: Mention that for very large datasets, a distributed approach like MapReduce can be used, showing awareness of scalability beyond a single machine. Also, explicitly state assumptions about tie-breaking (e.g., alphabetical order) to demonstrate attention to detail.
Ask about input size, definition of a word (e.g., case sensitivity, punctuation), and how to handle ties. This ensures you solve the correct problem.
Tokenize sentences into words, normalize case, and remove punctuation or stop words if required. This step ensures consistent counting.
Use a hash map to iterate through all words and count occurrences. This gives O(M) time where M is total number of words.
Use a min-heap of size N to keep track of the N most frequent words. Iterate through the frequency map, pushing and popping to maintain the heap, resulting in O(U log N) time where U is unique words.
Discuss time and space complexity, and handle edge cases like fewer than N unique words, ties, and empty input. Mention alternative approaches like sorting all words (O(U log U)) and when they might be preferable.
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