Ran out of time before I could even run a single test case.
First, clarify the problem constraints and confirm that BFS is appropriate for the traversal. Then, implement BFS to solve the base problem, analyze its time and space complexity, and identify the bottleneck. Finally, propose a binary search optimization on a monotonic property to reduce the time complexity, ensuring it fits within the strict time limit.
Pro tip: Always discuss trade-offs: binary search often requires a sorted or monotonic structure, so explain how you transform the problem to enable it. Also, mention that you would test edge cases and consider AWS-specific constraints like scalability and distributed processing.
Ask questions to understand the graph size, edge weights, and what 'optimize further' means. Confirm the time limit and whether binary search is expected on a specific parameter.
Write a clean BFS traversal, explaining how it explores the graph level by level. Analyze its time and space complexity (O(V+E)).
Discuss the bottleneck in BFS (e.g., repeated searches or large search space) and how binary search can reduce it by exploiting monotonicity.
Describe how to binary search over a range of possible answers (e.g., distance, time, or capacity) and use BFS as a feasibility check. Explain the new complexity (e.g., O((V+E) log N)).
Test with edge cases, compare the optimized solution to the base BFS, and mention any assumptions or limitations. Highlight how this meets the strict time limit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Treat the AI as a collaborative tool: first restate the problem and clarify requirements, then use the AI to explore approaches and edge cases while you drive the actual coding and reasoning. Verbally explain your thought process, validate AI suggestions critically, and implement the solution yourself, testing as you go.
Pro tip: At AWS, interviewers value ownership and customer obsession—so demonstrate that you own the final solution by questioning AI outputs and explaining trade-offs, rather than blindly accepting them. Also, proactively discuss scalability and edge cases, as AWS problems often involve distributed systems.
Restate the problem in your own words, ask clarifying questions about inputs, outputs, constraints, and edge cases. This shows systematic thinking and ensures you and the AI are aligned.
Use the AI to generate potential approaches, data structures, and algorithms, but evaluate each for time/space complexity and trade-offs. Discuss why you might choose one over another.
Write the code yourself, explaining each step. Use the AI to review small chunks or suggest improvements, but test frequently with sample inputs and edge cases to catch errors early.
Run through test cases, including edge cases, and debug systematically. If the AI suggests a fix, analyze it before applying, and explain why it works or doesn't.
After a working solution, discuss potential optimizations, alternative approaches, and scalability considerations. Summarize key learnings and how you leveraged the AI effectively.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.