Start by clarifying the problem and constraints, then propose at least two distinct approaches with trade-offs (time/space complexity, readability, scalability). Choose the optimal approach based on the constraints and implement it cleanly, explaining your reasoning and testing with edge cases.
Pro tip: Amazon values customer obsession and ownership: tie your choice to real-world impact, like scalability or maintainability, and mention how you'd test and iterate. Also, proactively discuss potential optimizations and trade-offs to show depth.
Ask clarifying questions to understand input size, edge cases, and performance expectations. This ensures you're solving the right problem and sets the stage for evaluating approaches.
Propose at least two different algorithms or data structures, such as brute force vs. optimized. Briefly outline each approach's logic and complexity.
Compare approaches on time/space complexity, readability, and scalability. Relate trade-offs to the problem constraints and Amazon's leadership principles (e.g., customer obsession, ownership).
Choose the approach that best balances efficiency and clarity for the given constraints. Write clean, modular code with meaningful variable names and comments.
Walk through test cases, including edge cases, and discuss potential optimizations or alternative implementations. Mention how you'd handle failures or scale the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a high-level architecture with clear components and data flow. Dive into the code flow for critical paths, discussing trade-offs and scalability. Conclude by summarizing how the design meets requirements and handles edge cases.
Pro tip: Always tie your design decisions back to Amazon's Leadership Principles, such as Customer Obsession and Dive Deep, to show alignment with company culture. Quantify trade-offs with metrics (e.g., latency, cost) to demonstrate practical judgment.
Ask questions to understand functional and non-functional requirements, such as scale, latency, consistency, and budget. Confirm assumptions with the interviewer before proceeding.
Sketch the main components (e.g., clients, services, databases, caches) and their interactions. Explain how data flows through the system to meet the requirements.
Walk through the code flow for key operations, such as request handling or data processing. Describe classes, methods, and algorithms, focusing on critical paths.
Discuss alternative designs and justify your choices based on trade-offs (e.g., consistency vs. availability, SQL vs. NoSQL). Explain how the system scales and handles failures.
Summarize how the design meets requirements and address potential edge cases, monitoring, and deployment considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.