← Bytedance Interview Insights
Went straight to a Trie and it clicked pretty fast.
Start by clarifying the problem constraints (board size, word count, word length) and then propose a solution that combines a Trie for efficient prefix matching with DFS backtracking on the board. Explain how the Trie prunes the search space, and discuss time/space complexity and potential optimizations like early termination and visited marking.
Pro tip: Mention that you can optimize by removing matched words from the Trie to avoid redundant searches, and that you should handle duplicate words in the input list. Also, be prepared to discuss trade-offs between using a Trie vs. sorting words and binary search.
Ask about board dimensions, number of words, word lengths, character set, and whether words can be reused. Confirm if output should be unique words and if the board can be modified.
Decide to build a Trie from the list of words for efficient prefix lookup. Use a set to store results and avoid duplicates. Consider using a visited matrix or modifying the board in-place to mark visited cells.
Iterate over each cell in the board and perform DFS if the character matches a Trie root child. During DFS, move in four directions, check Trie nodes, and when a word end is reached, add to results. Backtrack by unmarking visited cells.
Explain time complexity: O(M*N*4^L) worst-case, but Trie pruning reduces it. Space: O(total characters in words) for Trie. Mention optimizations: remove matched words from Trie, early termination if no children, and using a set for results.
Walk through a small example, then discuss edge cases: empty board, empty word list, single cell, words longer than board, and duplicate words. Verify correctness and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.