Got most of it but one test case slipped through.
First, clarify the problem and constraints, then discuss a brute-force approach and its time complexity. Next, explain how prefix and suffix sums can optimize the solution by precomputing cumulative sums to avoid redundant calculations. Finally, walk through the optimized algorithm, analyze its time and space complexity, and test with edge cases.
Pro tip: At Amazon, always tie your solution back to customer impact: mention how efficient algorithms reduce latency and scale to millions of users, aligning with Amazon's Leadership Principles like Customer Obsession and Deliver Results.
Ask clarifying questions to ensure you understand the input, output, constraints, and edge cases. Confirm the expected time and space complexity.
Propose a straightforward solution, analyze its time complexity, and identify inefficiencies that prefix/suffix sums can address.
Explain how to compute prefix and suffix sums in one or two passes, and how to use them to solve the problem efficiently.
Trace the algorithm on a sample input, including edge cases like empty array or single element, to demonstrate correctness.
State the final time and space complexity, and discuss potential optimizations or trade-offs. Write pseudocode or actual code if required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
All test cases passed and the feature worked end to end, so no complaints there.
Demonstrate a structured approach to AI-assisted coding by first clarifying requirements and constraints, then using AI to generate initial code while critically evaluating its correctness and efficiency. Emphasize testing, iteration, and explaining trade-offs to show you can leverage AI effectively without compromising engineering rigor.
Pro tip: Treat the AI as a junior pair programmer: verify its suggestions with tests and edge cases, and be ready to explain why you accepted or rejected its output. This shows you can use AI to accelerate work while maintaining high standards.
Ask clarifying questions to understand the feature's functional and non-functional requirements, such as input/output, edge cases, performance, and scalability. This ensures you and the AI are solving the right problem.
Outline a high-level design, including data structures and algorithms, before involving AI. This helps you guide the AI effectively and evaluate its suggestions against your plan.
Use the AI tool to generate code snippets or full implementations based on your plan. Prompt it with specific requirements and constraints to get relevant output.
Critically review the AI-generated code for correctness, efficiency, and style. Write tests, including edge cases, to validate the solution and identify any issues.
Refine the code based on test results and discuss alternative approaches, highlighting trade-offs in time/space complexity, readability, and maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.