The scoring part clicked pretty fast but I fumbled on the tie-breaking for a bit.
Start by clarifying the problem constraints and edge cases, then propose an efficient algorithm using a hash map for word frequencies and a heap for top N selection. Walk through the algorithm step-by-step, analyze time and space complexity, and discuss potential trade-offs between different approaches.
Pro tip: Demonstrate awareness of real-world scalability by mentioning how you would handle large datasets, such as using streaming or distributed processing, and emphasize the importance of tie-breaking rules in ranking problems.
Ask questions to confirm input formats, constraints (e.g., sentence length, number of queries, N value), and edge cases like empty sentences, no matches, or N larger than the number of sentences.
Propose an approach: preprocess query words into a set for O(1) lookups, iterate through each sentence to count occurrences of any query word, and store scores in a list of (score, index) pairs.
Use a min-heap of size N to keep track of the top N sentences by score, or sort the list if N is large. Ensure tie-breaking by ascending index is handled correctly.
Discuss time complexity: O(S * L + S log N) with heap, where S is number of sentences and L is average sentence length. Space complexity: O(S + Q) for scores and query set. Compare with sorting approach O(S log S).
Address edge cases: no matches (return empty or fewer than N), N=0, duplicate query words, and case sensitivity. Mention possible optimizations like early termination if N is small.
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