Started with a hashmap and just checked every possible prefix length for each input string.
Start by clarifying the requirements: are prefixes stored once and queried many times? Then propose a trie (prefix tree) as the core data structure, explaining how it enables efficient prefix matching. Discuss trade-offs with alternative approaches like sorted arrays with binary search or hash sets, and outline the algorithm for filtering input strings.
Pro tip: Mention that you would precompute a set of all prefixes if the stored prefix set is small and queries are frequent, but highlight that a trie is more memory-efficient for large prefix sets. Also, discuss how to handle edge cases like empty prefixes or duplicate inputs.
Ask about the expected size of the prefix set, the number of input strings, and whether prefixes can be updated dynamically. This determines the optimal data structure.
Explain that a trie stores prefixes efficiently, with each node representing a character. Mark nodes at the end of stored prefixes to indicate valid prefixes.
For each input string, traverse the trie character by character. If you reach a node marked as a prefix end before the string ends, the string matches; otherwise, it doesn't.
Compare with alternatives: a hash set of prefixes (O(1) lookup but requires checking all prefixes of each string), sorted array with binary search (O(log n) per prefix check), or Aho-Corasick for multiple pattern matching.
State time complexity: O(L) per string for trie traversal, where L is string length. Space: O(total characters in prefixes). Mention possible optimizations like compressing the trie (radix tree) or using a bloom filter for quick negative checks.
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