My first instinct was to just sort the characters of each word and use that as a lookup key, which works until it doesn't.
Clarify the problem constraints and edge cases, then propose an efficient algorithm using a hash map keyed by first and last characters to group vocabulary words. For each scrambled word, extract its first and last characters, retrieve the candidate group, and find the anagram by comparing sorted characters or character counts.
Pro tip: Mention that you can preprocess the vocabulary once to build the index, making each subsequent decode O(1) on average, and discuss how to handle duplicate anagrams or multiple matches by using additional constraints or returning any valid match.
Ask about input size, whether vocabulary words are unique, if scrambled words are guaranteed to have exactly one match, and how to handle case sensitivity or non-alphabetic characters.
Propose a hash map where keys are pairs of first and last characters (e.g., a tuple or a combined string) and values are lists of vocabulary words sharing those endpoints.
Iterate through the vocabulary list, compute the key for each word, and append the word to the corresponding list in the map.
For each scrambled word, compute its key, retrieve the candidate list, and find the anagram by comparing sorted characters or character frequency counts.
State the time and space complexity: O(V * L) preprocessing, O(S * L) decoding, where V and S are vocabulary and scrambled word counts, and L is average word length. Suggest optimizations like using character count arrays for faster anagram checks.
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