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A heap or priority queue helps efficiently retrieve the top K sentences based on frequency and lexicographic order. When many candidate sentences match a prefix, the heap ensures the highest ranked suggestions are returned quickly.
Yes, this problem is a classic system design and data structure question often discussed in FAANG-style interviews. It tests knowledge of Tries, priority queues, and designing efficient real-time data stream systems.
A Trie (prefix tree) is the most suitable data structure because it enables fast prefix lookups. Each node represents a character in the prefix and can store frequency data or top suggestions to quickly generate autocomplete results.
The optimal approach typically uses a Trie to store sentences along with their frequencies. This allows efficient prefix searches as the user types. Suggestions can then be retrieved using a heap or by maintaining the top results at each Trie node.