Straightforward once you realize you just need a set.
First, clarify the problem: each word is transformed into a Morse string by concatenating the Morse codes of its letters without separators. Then, use a hash set to store the unique Morse representations and return its size. This approach runs in O(N*L) time and O(N*L) space, where N is the number of words and L is the average word length.
Pro tip: Mention that the Morse code mapping can be stored as an array of 26 strings for O(1) lookup, and emphasize that using a set automatically handles duplicates. Also, note that the problem is essentially counting distinct transformations, which is a common pattern in string manipulation problems.
Confirm that each word's Morse translation is the concatenation of its letters' Morse codes without any separators, and that we need the count of distinct such translations across all words.
Create a mapping from each lowercase English letter to its Morse code string, e.g., using an array of 26 strings for constant-time access.
For each word, iterate through its characters, look up the corresponding Morse code, and concatenate them to form the word's Morse translation.
Insert each Morse translation into a hash set to automatically eliminate duplicates.
The size of the set is the number of distinct Morse translations. Return that integer.
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Clarify the problem constraints (e.g., dictionary size, string length, word reuse) and discuss a recursive backtracking solution with memoization to avoid redundant computations. Then, analyze time and space complexity, and consider optimizations like using a trie or dynamic programming for efficiency.
Pro tip: At Amazon, emphasize scalability and real-world applications (e.g., search query segmentation). Mention that you would test with edge cases like empty string, no valid segmentation, and overlapping words to ensure robustness.
Ask about input constraints (string length, dictionary size, word lengths), output format (list of lists of words), and whether words can be reused (yes, as stated).
Propose a backtracking solution: recursively try all prefixes of the remaining string that are in the dictionary, and recurse on the suffix. Use memoization to cache results for suffixes to avoid recomputation.
Discuss time complexity: O(2^n) without memoization, but with memoization it's O(n * L) where L is max word length, or O(n^2) if using a set for O(1) lookups. Space complexity: O(n) for recursion stack and memoization.
Suggest optimizations: use a trie for faster prefix lookups, or dynamic programming to build solutions bottom-up. Handle edge cases: empty string, no segmentation, and duplicate words in dictionary.
Walk through a simple example (e.g., 'catsanddog' with dict ['cat','cats','and','sand','dog']) to demonstrate correctness. Mention testing with large inputs and performance considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.