First, clarify the problem constraints and edge cases, then outline a brute-force solution before optimizing. Choose the right data structures and algorithms based on time/space complexity, and write clean, modular code with meaningful variable names. Test with provided examples and additional edge cases, and be prepared to explain your reasoning and trade-offs.
Pro tip: Amazon values customer obsession and ownership; during the OA, focus on writing correct, efficient code quickly, but also add comments explaining your thought process as if you're communicating with a teammate. If stuck, start with a working brute-force solution and then optimize, ensuring you at least pass some test cases.
Read the problem statement carefully, identify input/output formats, constraints, and edge cases. Ask clarifying questions if allowed.
Brainstorm approaches, start with a brute-force solution, then analyze time and space complexity to optimize. Choose appropriate data structures and algorithms.
Write clean, modular code with meaningful variable names and comments. Handle edge cases and ensure the solution is correct.
Run through provided examples and additional edge cases. Debug any issues and verify the solution meets the constraints.
If time permits, review for potential optimizations or alternative approaches. Ensure code is efficient and well-structured.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one surprised me more than the algorithm question.
Start by clarifying the task requirements and the expected output format, then outline a structured plan that includes data preparation, model interaction, and evaluation. Implement the solution with modular code, test edge cases, and discuss trade-offs such as latency, cost, and accuracy. Finally, reflect on how you would iterate and monitor the solution in production.
Pro tip: Demonstrate awareness of Amazon's leadership principles by emphasizing customer obsession (e.g., aligning model outputs with user needs) and ownership (e.g., handling failures gracefully). Also, mention specific evaluation metrics like BLEU or ROUGE for generative tasks, but note their limitations and the need for human evaluation.
Ask clarifying questions to understand the task scope, input/output formats, performance expectations, and any constraints like latency or cost. This ensures you build the right solution and shows you think before coding.
Outline a high-level plan: how you'll interact with the AI model (API calls, local inference), preprocess inputs, postprocess outputs, and handle errors. Consider modularity and testability.
Write clean, well-documented code with unit tests for key components. Test with sample inputs, including edge cases like empty responses or malformed outputs, and validate against expected behavior.
Define evaluation metrics (e.g., accuracy, F1, BLEU) and run experiments. Analyze results, identify weaknesses, and propose improvements such as prompt engineering or fine-tuning.
Articulate trade-offs between different approaches (e.g., API vs. self-hosted model, cost vs. performance). Discuss monitoring, logging, and scaling considerations for production deployment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.