Start by clarifying the problem constraints and edge cases, then propose a solution using a hash map for O(1) lookups. Discuss the algorithm's time and space complexity, and consider how to handle invalid characters or spaces.
Pro tip: Mention that you would preprocess the input to handle spaces and invalid characters, and consider using a StringBuilder for efficient string concatenation in languages like Java.
Ask about input constraints: uppercase English letters only? How to handle spaces, punctuation, or invalid characters? What should be the output format for letters and words?
Create a mapping from each letter to its Morse code. Use a hash map or an array of size 26 for efficient lookup.
Iterate through each character in the input string, look up its Morse code, and append it to the result with a space separator between letters.
Decide how to handle spaces (e.g., represent as a slash or multiple spaces) and invalid characters (skip or throw error). Ensure the output does not have trailing spaces.
State that the time complexity is O(n) where n is the length of the input string, and space complexity is O(n) for the output string.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the exact encoding format (e.g., spaces separate Morse letters, slashes separate words) and then design a decoder that splits the input, maps each Morse code to its character using a reverse lookup table, and joins the results. Discuss edge cases like invalid codes, extra spaces, and case sensitivity, and analyze time and space complexity.
Pro tip: Proactively mention that you would build the reverse mapping once and reuse it, and that you'd handle malformed input gracefully—this shows production-level thinking that Amazon values.
Ask whether letters are separated by single spaces and words by a different delimiter (e.g., '/'), and confirm the expected output format. This prevents ambiguity and shows attention to detail.
Create a hash map from Morse code strings to characters, either by inverting the standard Morse table or hardcoding it. This ensures O(1) lookup per code.
Split the input string by spaces (and word delimiters if applicable), then for each token, look up its character in the reverse map. Handle unknown codes by skipping or throwing an error as appropriate.
Join the decoded characters into words and words into a final string, preserving spaces between words. Consider trimming or normalizing whitespace.
State that time complexity is O(n) where n is the length of the input string, and space is O(1) for the lookup table plus O(n) for the output. Discuss edge cases like empty input, invalid Morse codes, and multiple spaces.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints (dictionary size, Morse string length, character set) and propose a solution that precomputes Morse encodings for dictionary words, then checks each against the input string. If the dictionary is large, consider a trie-based approach to prune comparisons early, and discuss trade-offs between time and space.
Pro tip: Mention that you would precompute Morse encodings once and store them in a hash set for O(1) lookups, but also note that if the dictionary is huge and the Morse string is long, a trie can avoid redundant prefix checks. This shows you think about scalability and real-world constraints.
Ask about the size of the dictionary, length of the Morse string, and whether the Morse code mapping is standard. Confirm that the output should be any single matching word, and discuss edge cases like empty strings or no match.
Convert each dictionary word into its Morse code representation using a mapping (e.g., A=.-, B=-...). Store these encodings in a data structure for efficient lookup.
For small dictionaries, iterate through precomputed encodings and compare to the input string. For large dictionaries, build a trie of Morse encodings to prune comparisons based on prefixes.
Compare time and space complexity of hash set vs. trie approaches. Discuss potential optimizations like early termination, parallel processing, or using a suffix automaton if multiple queries are expected.
Consider cases where no word matches, multiple words match, or the input string is empty. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as finding all paths in a trie of Morse-encoded dictionary words that exactly consume the input string. Use backtracking with memoization to avoid redundant exploration, and discuss trade-offs between time/space complexity and output size.
Pro tip: Clarify with the interviewer whether the dictionary words can be used multiple times and whether the output should be deduplicated; this shows attention to edge cases and prevents incorrect assumptions.
Ask about dictionary size, input length, whether words can repeat, and if the output should be unique sequences. This sets the stage for algorithm design.
Encode each dictionary word into Morse and insert into a trie. This allows efficient matching of prefixes of the input string.
Recursively explore all possible word segmentations by traversing the trie along the input string. Use memoization to cache results for suffixes to avoid recomputation.
Discuss time complexity in terms of input length and dictionary size, and space complexity for the trie and memoization. Mention that output size can be exponential, so the algorithm is output-sensitive.
Consider empty input, no valid segmentation, and duplicate words. Optimize by pruning trie branches that cannot lead to a solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.