Start by clarifying the problem statement and constraints, then outline a brute-force solution before optimizing. Focus on writing clean, modular code with edge cases in mind, and explain your thought process as you go.
Pro tip: Amazon OA problems often involve practical scenarios like optimizing delivery routes or managing inventory; relate your solution to real-world Amazon systems to show business impact.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss potential solutions, starting with a brute-force method, then analyze time and space complexity to identify optimizations.
Select the optimal approach, justify your choice, and outline the algorithm step-by-step before coding.
Write clean, well-structured code with meaningful variable names. Test with provided examples and additional edge cases.
Review for off-by-one errors, discuss potential improvements, and analyze final complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and expected behavior, then systematically explore the codebase to locate relevant files and understand the existing architecture. Implement the logic incrementally, writing tests to validate your changes, and ensure your solution aligns with the codebase's patterns and Amazon's engineering standards.
Pro tip: Demonstrate ownership by proactively identifying potential edge cases and discussing how you would handle them, and always consider the impact of your changes on other parts of the system.
Ask clarifying questions to fully understand the expected behavior, inputs, outputs, and any constraints. Confirm assumptions before diving into the code.
Navigate the repository structure, identify key modules, and trace the flow of data to understand where the new logic should be integrated. Look for existing patterns and conventions.
Outline your approach, breaking down the task into smaller steps. Write clean, maintainable code that follows the existing style and integrates seamlessly.
Write unit tests to cover the new logic and run existing tests to ensure no regressions. Consider edge cases and error handling.
Review your changes for quality, performance, and readability. Refactor if necessary and ensure the solution is scalable and aligns with best practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the system, then outline a high-level design before diving into details. Focus on scalability, availability, and trade-offs, and be prepared to justify your choices with data and examples.
Pro tip: Amazon values customer obsession and ownership, so tie your design decisions back to customer impact and long-term maintainability. Also, explicitly discuss how you would measure success and handle failures.
Ask questions to understand functional and non-functional requirements, such as scale, latency, consistency, and budget. Confirm assumptions with the interviewer.
Sketch the main components (e.g., clients, APIs, services, databases, caches) and how they interact. Keep it simple and focus on the core flow.
Elaborate on critical components: data model, partitioning, replication, load balancing, and caching. Discuss trade-offs and alternatives.
Identify potential bottlenecks and single points of failure, and explain how to mitigate them (e.g., sharding, redundancy, graceful degradation).
Summarize the design, discuss how to monitor and measure performance, and suggest future improvements or extensions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.