Went with DFS and the logic was solid, but their platform doesn't compile so I had no way to catch dumb stuff like typing the grid as int instead of char.
Start by clarifying the problem constraints and identifying whether BFS or DFS is more appropriate based on the goal (shortest path vs. exhaustive exploration). Treat each grid cell as a node and its valid neighbors (up, down, left, right, or diagonals) as edges, then apply the chosen traversal algorithm systematically. Always discuss time and space complexity before coding to demonstrate algorithmic maturity.
Pro tip: Amazon values scalability — proactively mention how your solution handles edge cases like disconnected components, obstacles, or very large grids, and consider whether an iterative approach (using an explicit stack/queue) is preferable over recursion to avoid stack overflow on large inputs.
Ask clarifying questions about grid size, cell values (obstacles, weights), start/end points, and whether diagonal movement is allowed. Confirm the exact goal — shortest path, number of islands, reachability, etc.
Decide between BFS (optimal for shortest path in unweighted grids) and DFS (better for exhaustive search, connected components, or cycle detection). Justify your choice aloud to show deliberate thinking.
Explicitly map the 2D grid to a graph: each cell (row, col) is a node, and valid adjacent cells are edges. Define boundary checks and obstacle conditions that determine valid neighbors.
Use a visited set or in-place marking to avoid revisiting cells and prevent infinite loops. Walk through your code with a small example to verify correctness before finalizing.
State that time complexity is O(M×N) and space complexity is O(M×N) for the visited structure and queue/stack. Mention potential optimizations like bidirectional BFS for shortest path or iterative DFS to reduce call stack overhead.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.