Warmup task but I still fumbled around longer than I should have.
Start by understanding the expected output and the maze representation, then systematically trace the print function with a small example to identify where the output diverges. Once the bug is found, explain the fix and verify it with edge cases like empty mazes or different path characters.
Pro tip: Demonstrate a methodical debugging process: verbalize your assumptions, test them with a minimal case, and use print statements or a debugger to isolate the issue. This shows you can handle ambiguous bugs in real codebases.
Ask about the expected output format, maze dimensions, and characters used for walls, paths, start, and end. Confirm whether the maze is a 2D array or grid of strings.
Create a small maze (e.g., 3x3) and manually compute the expected output. Run the print function mentally or with code to see the actual output and identify the discrepancy.
Walk through the print function line by line, checking loop bounds, indexing, and character mapping. Look for off-by-one errors, swapped dimensions, or incorrect conditionals.
Explain the root cause and suggest a specific code change. Ensure the fix addresses the issue without introducing new problems.
Test the fix with empty mazes, single-cell mazes, mazes with no solution, and different path characters to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, explain why BFS without visited tracking loops infinitely: cycles in the maze cause repeated enqueuing of the same cells. Then, describe adding a visited set or marking cells as visited when enqueued, ensuring each cell is processed at most once. Finally, discuss how this preserves BFS's shortest-path guarantee and prevents infinite loops.
Pro tip: Mention that marking cells as visited when enqueued (rather than when dequeued) avoids duplicate entries in the queue, which is more efficient and prevents redundant work. Also, note that if the maze is mutable, you can mark cells in-place to save memory, but be aware of side effects.
Explain that without tracking visited cells, BFS can revisit the same cell multiple times, especially in mazes with cycles, leading to an infinite loop.
Decide between a separate visited set (e.g., HashSet) or modifying the maze in-place (e.g., marking cells as walls or with a special value). Consider trade-offs like memory usage and side effects.
When exploring neighbors, check if a neighbor is already visited before enqueuing it. Mark the cell as visited either when enqueued or when dequeued, but enqueue-time marking is preferred to avoid duplicates.
Confirm that the modified BFS still finds the shortest path and runs in O(V+E) time, where V is the number of cells and E is the number of edges (connections between cells).
Test with mazes containing cycles, no path, and large sizes to ensure no infinite loops and correct results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where it got interesting and also where I started to sweat a little.
First, clarify the exact behavior of the one-way passages (e.g., which directions are allowed) and how they interact with the existing maze representation. Then, adapt the traversal algorithm to respect these directional constraints, ensuring the solution remains correct and efficient. Finally, discuss trade-offs such as handling cycles, performance implications, and potential edge cases.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that one-way passages can create directed cycles, so you must track visited states carefully to avoid infinite loops. Also, proactively discuss how you would test the solution with unit tests covering all directional combinations.
Ask questions to confirm the exact semantics of the new cell types: which directions are allowed, whether they are symmetric, and if they can be combined with existing cell types.
Decide how to represent the new cell types in the data structure, such as using an enum for cell types or a bitmask for allowed directions.
Modify the maze-solving algorithm (e.g., BFS/DFS) to only move in permitted directions from each cell, ensuring that the one-way constraint is enforced.
Consider scenarios like cycles, dead ends, and unreachable exits, and ensure the algorithm terminates correctly without infinite loops.
Discuss time/space complexity changes, potential performance impacts, and alternative approaches (e.g., graph transformation) with their pros and cons.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the maze as a graph where each state is (position, keys_collected). Use BFS to find the shortest path, since all moves have equal cost. Represent keys as a bitmask to efficiently track which keys have been collected.
Pro tip: Discuss how the state space grows exponentially with the number of keys, and mention optimizations like pruning visited states or using bidirectional BFS if the state space is too large.
Represent each state as (row, col, keys_bitmask), where keys_bitmask indicates which keys have been collected. This captures all necessary information to determine possible moves.
From a given state, generate valid moves: move to adjacent cells if not a wall, and if the cell contains a lock, only allow if the corresponding key is in the bitmask. If the cell contains a key, update the bitmask.
Use BFS to explore states level by level, ensuring the first time we reach the target, we have the shortest path. Maintain a visited set of (row, col, keys_bitmask) to avoid cycles.
Time complexity is O(R*C*2^K), where K is number of keys. Discuss trade-offs: BFS guarantees shortest path but may be memory-intensive; A* with admissible heuristic could be faster but more complex.
If K is large, consider pruning: e.g., only keep states with minimal steps for a given (position, keys). Or use bidirectional BFS if start and end are known. Mention that in practice, K is often small.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.