The problem statement was deliberately vague and I had to ask a bunch of clarifying questions before I even understood what was being asked.
Use a backtracking (recursive DFS) approach to explore all possible combinations, pruning branches early when the running sum exceeds the target. Track the current combination in a list and add it to the results when the sum exactly equals the target, ensuring you also handle duplicates and edge cases like empty sets or zero targets.
Pro tip: Mention upfront that you'll sort the input array first — this single optimization enables powerful early termination (pruning) and makes duplicate handling trivial, which signals to the interviewer that you think about efficiency before writing code.
Ask whether elements can be reused, whether the input contains duplicates, and whether negative numbers are possible. Confirm if all valid combinations are needed or just one, as this affects the algorithm design.
Propose a backtracking/DFS approach and explain why it fits: you need to enumerate subsets, and backtracking lets you explore and prune the search space efficiently. Briefly contrast it with DP (which tells you *if* a sum exists but not easily *which* elements).
Sort the array to enable pruning and duplicate skipping, then define a recursive helper that takes the remaining target, a start index, and the current path. At each level, iterate from the start index forward, adding elements and recursing.
Add a base case: if remaining target equals zero, record the current path as a valid combination. Break out of the loop early if the current element exceeds the remaining target (possible because the array is sorted), and skip duplicate elements at the same recursion level.
State the time complexity as O(2^n) in the worst case (exponential due to subset enumeration) and O(n) auxiliary space for the recursion stack. Walk through at least two test cases: a normal case and an edge case (e.g., no valid combination, single-element match, or all duplicates).
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This was the round that felt less like an interview and more like two engineers talking.
Choose a project that genuinely showcases your engineering depth — ideally one with real users, measurable impact, or non-trivial technical complexity. Structure your answer to move from problem context to design decisions to honest self-critique, demonstrating that you think like a senior engineer who owns outcomes, not just code. Amazon values builders who can articulate *why* they made choices, not just *what* they built.
Pro tip: Proactively naming your limitations and trade-offs before being asked signals engineering maturity and intellectual honesty — two traits Amazon explicitly looks for in its Leadership Principles ('Have Backbone' and 'Learn and Be Curious'). Candidates who only highlight successes come across as less credible than those who can say 'here's what I'd do differently.'
Briefly describe the problem you were solving, who it was for, and why it mattered. Keep this to 2-3 sentences so you spend most of your time on the engineering substance.
Walk through 2-3 concrete architectural or technical choices you made — such as technology stack selection, data modeling, or API design — and explain the reasoning behind each one. Focus on *why* you chose one approach over alternatives, not just what you chose.
For each major decision, explicitly name what you gave up — e.g., 'I chose simplicity over scalability here because the user base was small and iteration speed mattered more.' This demonstrates systems thinking and honest engineering judgment.
Identify 1-2 real limitations of the current system — performance bottlenecks, lack of test coverage, scalability ceilings, or technical debt. Be specific rather than vague to show you've genuinely reflected on the work.
End by stating what you would change if you rebuilt it today, tying it back to lessons learned. This demonstrates growth mindset and the ability to self-correct — qualities Amazon highly values.
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Came up early and I'd actually prepared for this one because I'd seen it mentioned elsewhere.
Frame your answer around concrete, specific examples of AI tools you actively use, emphasizing how they amplify your productivity and code quality rather than replace your judgment. Demonstrate adaptability by showing you continuously evaluate and adopt new tools, while also being thoughtful about their limitations. Tie your usage back to delivering results faster and at higher quality, which aligns with Amazon's customer obsession and delivery principles.
Pro tip: Avoid sounding like you blindly rely on AI — Amazon values strong engineering judgment, so explicitly mention how you validate AI-generated output, catch its mistakes, and maintain ownership of the final solution. This signals maturity and separates you from candidates who treat AI as a magic black box.
Open by briefly listing the specific AI tools you use (e.g., GitHub Copilot, ChatGPT, Amazon CodeWhisperer, Cursor). Being specific immediately establishes credibility and shows you are actively engaged with the current landscape.
Walk through 2-3 distinct scenarios where you applied AI tools, such as accelerating boilerplate generation, debugging tricky errors, writing unit tests, or exploring unfamiliar APIs. Ground each use case in a real task to make your answer tangible and believable.
Explain how you review, test, and validate AI-generated suggestions rather than accepting them blindly. Mention catching bugs, security issues, or style inconsistencies in AI output to demonstrate engineering ownership.
Share a measurable or observable outcome, such as reduced time spent on repetitive tasks, faster onboarding to a new codebase, or improved test coverage. Connecting AI usage to business or team impact resonates strongly with Amazon's results-oriented culture.
Close by mentioning how you stay current with evolving AI tooling and how you share useful techniques with teammates. This demonstrates adaptability and the leadership principle of 'Learn and Be Curious.'
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Start by acknowledging the ambiguity openly and then systematically narrow the problem space by defining assumptions, constraints, and success metrics before jumping into a solution. Use prompt engineering principles — such as decomposition, iterative refinement, and context-setting — as an explicit lens to structure your feature design. Demonstrate that you can drive clarity from chaos while keeping the customer outcome at the center.
Pro tip: Amazon deeply values the 'Work Backwards' methodology — start from the customer experience and the ideal press release, then engineer backward to the solution. Explicitly naming this approach and tying your prompt engineering design decisions to customer value signals strong cultural alignment.
Ask targeted clarifying questions to define the problem boundaries: Who is the target user? What is the core pain point? What does success look like in 6 months? Explicitly state any assumptions you are making to show structured thinking.
Establish how prompt engineering principles apply — explain that you will treat the feature design like crafting a high-quality prompt: providing clear context, constraints, examples (few-shot), and an iterative feedback loop. This frames your entire approach with a coherent mental model.
Break the large feature into smaller, independently deliverable sub-problems, similar to chain-of-thought decomposition in prompting. Prioritize components by customer impact and technical feasibility, identifying quick wins versus long-term investments.
Propose a concrete MVP design that can be tested and refined — analogous to prompt iteration cycles where you evaluate output, gather feedback, and refine the input. Include mechanisms for measuring quality (e.g., A/B testing, user feedback loops, guardrails for edge cases).
Proactively identify failure modes, edge cases, and trade-offs (e.g., latency vs. accuracy, flexibility vs. consistency), and explain how you would mitigate them. Discuss how the solution scales to Amazon's level of traffic and diverse customer segments.
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Started with BFS and returned a count, then got asked to return the nodes with distances.
Use Breadth-First Search (BFS) for unweighted graphs or Dijkstra's algorithm for weighted graphs to explore nodes level by level, tracking cumulative distances from the starting node. Maintain a results map to record each reachable node and its actual distance, stopping exploration once the distance threshold is exceeded. Clarify upfront whether the graph is weighted or unweighted, as this determines the optimal algorithm choice.
Pro tip: Mentioning the trade-off between BFS (O(V+E) for unweighted) and Dijkstra's (O((V+E) log V) for weighted) shows algorithmic maturity — Amazon values candidates who reason about complexity and choose tools deliberately rather than defaulting to a single solution.
Ask whether the graph is weighted or unweighted, directed or undirected, and whether cycles are possible. Confirm if the threshold is inclusive (≤) and whether the starting node itself should be included in results with distance 0.
Select BFS for unweighted graphs since each edge has equal cost, or Dijkstra's algorithm for weighted graphs with non-negative edge weights. Mention that Bellman-Ford would be needed if negative weights exist.
Use a queue (BFS) or min-heap priority queue (Dijkstra's) initialized with the starting node at distance 0. Maintain a visited set and a distance map, and only enqueue neighbors whose cumulative distance does not exceed the threshold.
After traversal completes, return all entries from the distance map where the recorded distance is within the threshold, including the starting node at distance 0. Ensure the output format matches the expected structure (e.g., list of tuples or dictionary).
State time complexity (O(V+E) for BFS, O((V+E) log V) for Dijkstra's) and space complexity O(V). Discuss edge cases such as disconnected graphs, a threshold of 0, self-loops, and a starting node with no neighbors.
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Use the STAR method to tell a compelling story that highlights Amazon's Leadership Principles — specifically 'Ownership' and 'Bias for Action' — by describing a concrete situation where you identified a gap or opportunity outside your defined responsibilities and took initiative to address it. Focus on the measurable impact your actions had on the team, product, or business. Avoid framing it as overstepping; instead, position it as taking ownership of a broader outcome.
Pro tip: Amazon deeply values the 'Ownership' principle — the idea that leaders never say 'that's not my job.' Explicitly tie your story to a business outcome (e.g., reduced latency, saved costs, improved team velocity) to show you think like an owner, not just an executor.
Briefly describe your official role and the project context so the interviewer understands the boundaries of your defined responsibilities. Keep this concise — one to two sentences max.
Explain what you noticed that was outside your scope — a problem, risk, or improvement opportunity that others had overlooked or deprioritized. Clarify why it mattered to the team or business if left unaddressed.
Detail the specific steps you took beyond your role, including any cross-functional collaboration, self-directed learning, or extra effort involved. Emphasize that you acted without being asked and explain how you managed your core responsibilities simultaneously.
Acknowledge any obstacles you faced — such as pushback, resource constraints, or ambiguity — and explain how you navigated them. This demonstrates resilience and mature judgment.
Share the concrete, measurable outcome of your initiative — such as performance improvements, cost savings, reduced incidents, or team efficiency gains. Connect the result back to a broader business or customer impact.
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I used the same project across two rounds and realized mid-answer in the second one that I'd already told a version of this story.
Use a specific, technically rich project that showcases your ability to navigate ambiguity, make architectural trade-offs, and deliver results at scale — all of which align with Amazon's Leadership Principles. Structure your answer to clearly separate the sources of complexity, your decision-making process, and a candid retrospective that demonstrates growth and self-awareness. Avoid vague generalities; concrete metrics, technologies, and outcomes will make your answer memorable.
Pro tip: Amazon interviewers are specifically listening for 'Dive Deep' and 'Learn and Be Curious' — so your 'what I'd do differently' section is not a weakness trap, it's your opportunity to show engineering maturity; candidates who give a thoughtful, technically specific retrospective consistently outperform those who say 'I wouldn't change much.'
Briefly introduce the project — its business purpose, your role, team size, and timeline. Give just enough context so the interviewer understands the stakes and scope without getting lost in background details.
Articulate the specific dimensions that made the project complex — technical (e.g., distributed systems, data consistency, scale), organizational (e.g., cross-team dependencies, ambiguous requirements), or both. Be precise: name the technologies, constraints, and competing priorities involved.
Describe the concrete actions you took to manage the complexity — how you broke down the problem, prioritized trade-offs, coordinated stakeholders, and mitigated risks. Highlight key decisions and the reasoning behind them, especially where you chose one approach over another.
Quantify the results wherever possible — performance improvements, reliability metrics, business impact, or delivery timeline. Connect the outcome back to the decisions you made to reinforce that your approach was effective.
Offer a specific, technically grounded reflection on what you would do differently — whether it's an architectural choice, a process improvement, or an earlier escalation. Frame it as a lesson learned rather than a failure, and explain how it has shaped your current engineering thinking.
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Started with a BFS approach similar to minimum knight moves but the board size forced lazy neighbor generation instead of building the full graph upfront.
Start by clarifying the problem constraints (board size, piece types, start/end positions, obstacles) and then propose a graph-based pathfinding approach using BFS or A* where each cell is a node and edges are generated dynamically based on piece movement rules. Emphasize abstracting piece movement into a common interface so the algorithm remains agnostic to piece type, then discuss optimizations needed for very large boards.
Pro tip: Proactively distinguish between fixed-offset pieces (knight, king) and sliding pieces (rook, bishop, queen) when discussing edge generation — sliding pieces require ray-casting with early termination on blockers, which is a common performance pitfall interviewers look for candidates to identify.
Ask about board dimensions, whether the board is sparse or dense with obstacles, whether multiple piece types coexist, and if the goal is shortest path or any valid path. Confirm if the board fits in memory or requires on-demand generation.
Design a PieceMoveGenerator interface with a method like getReachableCells(position, board) that each piece type implements. This decouples the pathfinding algorithm from movement logic and makes the system extensible.
Use BFS for unweighted shortest path or A* with a Manhattan/Chebyshev distance heuristic for weighted/large boards. Represent the board as an implicit graph, generating neighbors on-the-fly rather than pre-building an adjacency list to handle very large boards efficiently.
For sliding pieces, implement ray-casting in each valid direction, iterating cell by cell and stopping when a blocker or board boundary is hit. Ensure O(n) per direction rather than re-scanning the entire board.
Address memory constraints by using a visited hash set instead of a full board matrix, and consider bidirectional BFS or hierarchical pathfinding (e.g., HPA*) for extremely large boards. Mention caching frequently queried positions if the board is static.
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