Two of the three technical rounds had hard-level problems and this was one of them.
Clarify the problem constraints and edge cases, then identify it as a topological sort problem (likely requiring cycle detection). Discuss both Kahn's algorithm (BFS) and DFS-based approaches, explaining trade-offs, and implement the most suitable one with clear time/space complexity analysis.
Pro tip: Amazon values customer obsession and ownership: after solving, discuss how you'd handle large-scale inputs (e.g., streaming graph updates) and mention potential optimizations like parallel processing or early cycle detection to prevent downstream failures.
Restate the problem in your own words, ask clarifying questions about input size, graph representation, and expected output (e.g., any valid order or lexicographically smallest).
Recognize it as a topological sort problem; decide between Kahn's algorithm (BFS) and DFS-based topological sort, considering cycle detection and constraints.
Outline the algorithm step-by-step, including data structures (adjacency list, indegree array, queue/stack), and analyze time and space complexity.
Write clean, modular code with meaningful variable names; walk through a small example and test edge cases like empty graph, single node, and cycles.
Discuss potential optimizations (e.g., using a priority queue for lexicographical order) and trade-offs between BFS and DFS approaches in terms of readability and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints, then define the state and recurrence relation before coding. Implement the DP iteratively with optimizations, and test with edge cases while explaining time and space complexity.
Pro tip: Amazon values customer obsession and ownership; relate your DP solution to real-world scalability and efficiency, and proactively discuss trade-offs like space optimization to show you think beyond just passing tests.
Ask clarifying questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Identify what each state represents (e.g., dp[i] = max profit up to i) and how it captures subproblems. Explain why this state is sufficient.
Formulate how to compute a state from previous states. Discuss base cases and initialization.
Code the solution iteratively, using memoization or tabulation. Consider space optimization if applicable (e.g., rolling array).
Walk through time and space complexity, then test with provided examples and edge cases. Discuss potential improvements or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Every single round had these, not just one.
Prepare 6-8 STAR stories that map to Amazon's Leadership Principles, focusing on Adaptability & Ambiguity and Stakeholder Management. For each story, explicitly name the LP it demonstrates and quantify the impact. Practice delivering these stories concisely (2-3 minutes) and be ready to dive deep into any aspect.
Pro tip: Amazon interviewers are trained to probe for specifics—be ready to answer follow-up questions about your exact actions, metrics, and what you would do differently. Use the 'I' not 'we' to highlight your individual contribution.
Identify 6-8 diverse experiences that showcase different Leadership Principles, especially Adaptability & Ambiguity and Stakeholder Management. Ensure each story has a clear Situation, Task, Action, and Result.
For each story, succinctly describe the situation and task, then spend most time on your specific actions and the measurable results. Highlight how you navigated ambiguity or managed stakeholders.
Include metrics (e.g., % improvement, time saved, revenue impact) to make your results concrete. If exact numbers are unavailable, use reasonable estimates and explain how you measured success.
Anticipate follow-up questions that probe deeper into your actions, decisions, and learnings. Rehearse concise answers that provide additional detail without rambling.
Emphasize customer obsession, ownership, and bias for action in your stories. Show how you embody Amazon's peculiar ways of thinking and working.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.