I went straight to backtracking and it was fine, but the follow-up about pruning is where I got a little fuzzy.
Start by clarifying the problem constraints (grid size, word length, character set) and then propose a backtracking DFS that explores all four directions from each cell, marking visited cells to avoid reuse. After explaining the basic algorithm and its O(N * 3^L) time complexity, discuss pruning techniques like early termination on character mismatch and frequency checks, and mention iterative improvements such as using a stack or bitset for visited tracking.
Pro tip: Emphasize that in production ML systems, such as those at PayPal, you'd often preprocess the grid into a trie or use bidirectional search to handle multiple queries efficiently, showing you think beyond the single-query interview scenario.
Ask about grid dimensions, word length, character set, and whether multiple queries are expected. Discuss edge cases like empty grid, empty word, or word longer than total cells.
Explain that you iterate over each cell as a starting point, and recursively explore all four directions, marking cells as visited and unmarking on backtrack. Base case: if all characters matched, return true.
State that worst-case time is O(N * 3^L) where N is number of cells and L is word length (since first move has 4 options, subsequent have 3), and space is O(L) for recursion stack plus O(N) for visited tracking.
Mention pruning: early exit if current cell doesn't match, check if word length exceeds total cells, and use frequency count to ensure grid has enough characters. Also suggest iterative DFS with explicit stack to avoid recursion limits.
Talk about using a bitset or in-place marking to reduce space, and for multiple queries, building a trie of words or using bidirectional BFS. Discuss trade-offs between preprocessing time and query time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.