My first instinct was to just build the digit string for each word and throw everything into a hashmap keyed by that string.
Clarify the T9 mapping and edge cases (e.g., case sensitivity, non-letter characters), then propose a hash map solution that encodes each word to its digit string and groups words by that key. Discuss time and space complexity, and consider optimizations like early filtering or streaming for large inputs.
Pro tip: Mention that you can avoid encoding every word fully by using a trie or by hashing prefixes, but the hash map approach is optimal for most cases. Also, proactively discuss how to handle ties or ordering of groups if the output format is unspecified.
Ask about the exact T9 mapping, whether words are case-sensitive, and how to handle non-alphabetic characters. Confirm the output format (e.g., list of lists) and if the order of groups matters.
Create a mapping from letters to digits (e.g., using a dictionary or array). For each word, convert it to a digit string by iterating over its characters and looking up the corresponding digit.
Use a hash map where the key is the digit string and the value is a list of words. Iterate through the input list, compute the digit string for each word, and append the word to the corresponding list.
After processing all words, iterate through the hash map and collect only those lists that contain two or more words. Return the resulting list of groups.
State that the time complexity is O(N * L) where N is the number of words and L is the average length, and space is O(N * L) for the hash map. Mention possible optimizations like early termination if a word's encoding cannot match any existing group, or using a trie for prefix-based grouping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.