Started by poking at the URL to figure out what responses looked like before writing any code.
Model the maze as a graph where each URL is a node and open directions are edges. Use BFS to explore the maze level by level, tracking visited URLs to avoid cycles, and stop when you reach the exit. Discuss how you would detect the exit and handle API failures or rate limits.
Pro tip: Before coding, clarify with the interviewer how the exit is represented (e.g., a specific URL pattern or a response indicating no open directions) and whether the API is reliable. This shows you think about edge cases and system constraints.
Ask questions to understand the API response format, how the exit is identified, and any constraints like rate limits or authentication. Confirm whether the maze is static or dynamic.
Treat each URL as a node and open directions as edges. Choose BFS for shortest path or DFS for simplicity, and explain your choice based on the problem requirements.
Write pseudocode or code for BFS/DFS, using a queue/stack and a visited set to avoid infinite loops. At each step, call the API to get open directions and enqueue new URLs.
Define how to detect the exit (e.g., specific URL or response). Implement retry logic for API failures and consider rate limiting by adding delays or batching requests.
Discuss time and space complexity in terms of number of cells and connections. Suggest optimizations like bidirectional search or caching API responses if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.