My first instinct was DFS, trying each possible letter at each digit position and checking against the word list.
Start by clarifying the problem constraints (e.g., word list size, digit string length, whether words can be reused) and the phone keypad mapping. Then present a backtracking solution that builds digit sequences for each word and compares, followed by an optimized trie-based approach that traverses the trie according to the digit string to prune unnecessary paths. Discuss trade-offs in time/space complexity and when each approach is preferable.
Pro tip: Emphasize the importance of pre-processing the word list into a trie for the optimized solution, as it reduces the search space significantly and is a common pattern in string matching problems. Also, mention that you would handle edge cases like empty input and digits '0' and '1' which have no letters.
Ask about input sizes, whether the word list is static or dynamic, and if the output order matters. Confirm the phone keypad mapping and that '0' and '1' have no letters.
For each word, generate its digit sequence by mapping each letter to its corresponding digit, then compare with the input digit string. Alternatively, for each digit, backtrack over possible letters to form words and check against the word list.
Build a trie from the word list. Then traverse the trie using the digit string: at each digit, explore all child nodes corresponding to letters mapped to that digit. Collect words at leaf nodes that match the entire digit string.
Compare time and space complexity: backtracking per word is O(N * L) where N is number of words and L is average length; trie approach is O(D * 4^D) worst-case but pruned by trie, often faster for large word lists. Discuss memory overhead of trie.
Address empty word list, empty digit string, digits '0' and '1', and duplicate words. Summarize when to use each approach based on constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.