Start by clarifying the problem, including input/output formats, constraints, and edge cases. Then discuss a brute-force approach and optimize it, explaining time and space complexity. Finally, implement clean code, test with examples, and consider Amazon's leadership principles like Customer Obsession and Dive Deep.
Pro tip: Think aloud to demonstrate your problem-solving process, and proactively discuss trade-offs between different solutions. This shows maturity and aligns with Amazon's emphasis on ownership and deep analysis.
Ask clarifying questions to ensure you fully understand the problem, including input/output, constraints, and edge cases. Restate the problem in your own words to confirm.
Discuss a brute-force solution first, then optimize by identifying bottlenecks. Explain the reasoning behind each approach and compare their time and space complexities.
Outline the steps of your chosen algorithm, including data structures and any helper functions. Consider potential pitfalls and how to handle edge cases.
Write clean, modular code with meaningful variable names. Verbalize your thought process as you code, and handle edge cases explicitly.
Walk through your code with a few test cases, including normal, edge, and large inputs. If time permits, discuss how you would further optimize or refactor.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got the base graph problem right, felt good for about 30 seconds.
Start by clarifying the graph problem, including input format, constraints, and expected output. Choose an appropriate representation (adjacency list/matrix) and algorithm (BFS/DFS/Dijkstra) based on the problem. For the follow-up, identify the variation (e.g., weighted edges, directed graph, additional constraints) and adapt your solution, discussing trade-offs.
Pro tip: Demonstrate Amazon's Leadership Principles by proactively discussing edge cases, scalability, and customer impact. For example, mention how your solution handles large graphs efficiently and how it benefits the end-user.
Ask questions to understand the graph type (directed/undirected, weighted/unweighted), constraints (size, edge cases), and expected output. Confirm with the interviewer before proceeding.
Choose a graph representation and algorithm. Explain your reasoning, including time and space complexity. Consider trade-offs between different approaches.
Write clean, modular code with meaningful variable names. Handle edge cases and test with a small example to verify correctness.
Identify how the variation changes the problem. Determine if your original solution can be adapted or if a new algorithm is needed. Discuss the impact on complexity.
Modify your code or write a new solution for the variation. Test with examples and discuss potential optimizations or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to structure your answer, focusing on a situation where you had to navigate ambiguity or adapt to change. Emphasize how you took ownership, made decisions with incomplete information, and delivered results. Connect your actions to Amazon's Leadership Principles, especially 'Customer Obsession' and 'Bias for Action'.
Pro tip: Amazon interviewers value data-driven decisions and measurable impact. Quantify your results and explicitly tie your actions back to specific Leadership Principles to show alignment with Amazon's culture.
Briefly describe the situation, including the ambiguity or change you faced. Provide enough context for the interviewer to understand the challenge.
Clearly state the problem or goal, and why it was ambiguous or required adaptability. Highlight the stakes and any constraints.
Explain the steps you took to navigate the ambiguity, such as gathering data, making assumptions, or pivoting. Focus on your specific contributions and decision-making process.
Share the results of your actions, including measurable impact. If possible, mention what you learned and how you applied it to future situations.
Explicitly tie your story to Amazon's Leadership Principles, such as 'Learn and Be Curious' or 'Deliver Results', to demonstrate cultural fit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.