Starting with broken tests instead of a blank slate threw me off more than I expected.
Start by carefully reading the problem statement and the failing test cases to understand the expected behavior. Then, systematically debug the code by tracing through the logic, identifying discrepancies, and fixing them one by one. Finally, run the tests to ensure all pass and consider edge cases.
Pro tip: Demonstrate a methodical debugging process: verbalize your hypotheses and how you'll test them. This shows structured thinking and effective communication, which are highly valued at Meta.
Read the problem description and examine the failing test cases to determine what the code should do and where it fails.
Walk through the code logic, either manually or with a debugger, to pinpoint the exact lines causing incorrect behavior.
Make one fix at a time, re-running tests after each change to isolate the impact and avoid introducing new issues.
After all tests pass, consider additional edge cases (e.g., empty maze, single cell) to ensure robustness.
If time permits, clean up the code and discuss potential optimizations or trade-offs in the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew the answer but fumbled the explanation a bit.
Explain that BFS explores nodes level by level using a queue, and without a visited set, nodes can be enqueued multiple times, leading to cycles and infinite loops. Emphasize that the visited set prevents revisiting nodes, ensuring termination and efficiency.
Pro tip: Mention that the visited set should be checked when enqueuing, not just when dequeuing, to avoid redundant work and potential infinite loops in graphs with cycles.
Briefly describe BFS as a graph traversal algorithm that explores neighbors level by level using a queue.
State that the visited set tracks nodes already explored to prevent reprocessing and ensure termination.
Explain that in a graph with cycles (like a maze), nodes can be revisited and re-enqueued indefinitely, causing an infinite loop.
Use a two-node cycle (A<->B) to show how BFS without visited set would enqueue A, then B, then A again, repeating forever.
Summarize that the visited set is essential for BFS to terminate and run efficiently on graphs with cycles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward once I stopped overthinking it.
First, clarify the original maze traversal algorithm and its assumptions about movement. Then, analyze how restricting movement to left and right affects the state space, graph representation, and traversal strategy, and discuss the implications for complexity and correctness.
Pro tip: Demonstrate awareness that this restriction often reduces the problem to a 1D or per-row traversal, which can simplify the solution but may require handling of disconnected components or unreachable areas.
Restate the original maze traversal problem, including allowed movements (e.g., up, down, left, right) and the goal (e.g., find a path from start to end).
Explain how limiting movement to left and right changes the graph: each row becomes a separate 1D line, and vertical connections are removed. This may disconnect the maze into independent rows.
Describe how to modify the algorithm: for each row, perform a linear scan or BFS/DFS along the row to find reachable cells. If the start and end are in different rows, the goal is unreachable unless there's a way to change rows (which is not allowed).
Analyze time and space complexity: often O(rows * cols) or O(cells) if scanning each row. Mention edge cases: start and end in same row, obstacles blocking path, multiple disconnected segments.
Summarize how the restriction simplifies the problem but may make it trivial or impossible depending on start/end positions. Highlight that the solution becomes more efficient but less general.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a state-space search where each state includes the current position and the set of keys collected. Use BFS to find the shortest path, since all moves have equal cost, and represent keys as a bitmask for efficient state encoding. Discuss trade-offs between BFS and other algorithms, and consider optimizations like bidirectional search or A* with admissible heuristics.
Pro tip: Mention that the state space is O(R*C*2^K) and can be large, so pruning visited states with the same position and key set is crucial; also note that if keys are reusable, the bitmask approach works, but if keys are consumed, the state must track remaining keys.
Ask about grid size, number of keys, whether keys are reusable, and if multiple keys of the same type exist. Confirm if doors require a specific key and if keys can be dropped.
Represent each state as (row, col, keys_bitmask). Use a bitmask to efficiently track which keys have been collected, assuming up to 10-15 keys.
Use BFS for unweighted shortest path. For larger grids, consider A* with a heuristic like Manhattan distance to the nearest key or door, but ensure admissibility.
Queue states, and for each state, explore neighbors. If neighbor is a door, check if the corresponding key is in the bitmask; if not, skip. If neighbor is a key, update the bitmask. Mark visited states to avoid cycles.
Time complexity O(R*C*2^K), space O(R*C*2^K). Discuss pruning, bidirectional BFS, or using Dijkstra if movement costs vary. Mention that if K is large, the problem becomes NP-hard, so heuristics or approximations may be needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.