Picked Minesweeper because I could narrate the state machine fast.
Choose a simple, well-understood game (e.g., Tic-Tac-Toe, Connect Four, or a basic card game) to keep the focus on design principles rather than game complexity. Structure your answer by first clarifying requirements and scope, then walking through the core rules, object model, and extension points, while highlighting trade-offs and Amazon leadership principles like Customer Obsession and Ownership.
Pro tip: Explicitly state your assumptions and scope early, and tie design decisions back to Amazon's leadership principles (e.g., 'I'm choosing a simple game to prioritize delivering a working design quickly, which reflects Bias for Action'). This shows you understand Amazon's culture and can make pragmatic trade-offs.
Ask clarifying questions to define the game's target audience, platform, and constraints (e.g., single-player vs. multiplayer, real-time vs. turn-based). State your assumptions and choose a simple game to keep the discussion focused.
Outline the game's objective, rules, win/loss conditions, and player interactions. Keep it concise and ensure the rules are unambiguous.
Identify key entities (e.g., Game, Player, Board, Move) and their relationships, responsibilities, and interfaces. Use UML-like diagrams or clear verbal descriptions to illustrate the model.
Explain how you would extend the game (e.g., new rules, AI opponents, multiplayer support) and the design patterns or architectural choices that enable this. Highlight trade-offs between simplicity and flexibility.
Recap the design, emphasizing how it aligns with Amazon's leadership principles (e.g., Customer Obsession, Ownership, Invent and Simplify). Invite feedback and discuss potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
BFS over DFS, and say it out loud before writing anything.
Start by clarifying the problem: flood-fill reveals contiguous empty cells and their adjacent numbered cells. Then describe a BFS/DFS algorithm with a queue or stack, marking visited cells to avoid cycles, and discuss time/space complexity. Finally, mention optimizations like iterative BFS to avoid recursion limits and early termination when no empty cells remain.
Pro tip: Emphasize that you would use an iterative BFS with a queue to prevent stack overflow in large grids, and that you would only enqueue empty cells while revealing adjacent numbered cells without enqueuing them. This shows awareness of production constraints and edge cases.
Confirm that clicking an empty cell should reveal all connected empty cells and their adjacent numbered cells. Ask about grid size, recursion limits, and whether diagonal connections count.
Select BFS or DFS for traversal. Explain that BFS with a queue is often preferred for large grids to avoid recursion depth issues, but DFS with an explicit stack is also valid.
Describe initializing a queue with the clicked cell, marking it visited, and while the queue is not empty: dequeue a cell, reveal it, and for each neighbor, if empty and unvisited, mark and enqueue; if numbered, reveal but do not enqueue.
State that time complexity is O(N) where N is the number of cells in the connected region, and space is O(N) for the queue/visited set. Mention edge cases like clicking a numbered cell (no flood fill) or already revealed cells.
Mention using a boolean array for visited instead of a set for efficiency, early termination if all empty cells are revealed, and potential for iterative deepening if memory is a concern.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said 'all mines are flagged' at first and the interviewer just waited.
Clarify that the win condition is revealing all non-mine cells without detonating any mine. Then discuss efficient tracking by maintaining a counter of remaining safe cells, decrementing on each reveal, and checking for zero to declare victory in O(1) time.
Pro tip: Mention that the counter approach avoids scanning the board after each move, which is crucial for large boards and aligns with Amazon's leadership principle of 'Invent and Simplify'.
State that the player wins when all non-mine cells are revealed and no mine has been detonated. Emphasize that revealing all mines is not required.
Explain that one could check after each move whether any unrevealed non-mine cell remains, but this would be O(N) per move, leading to O(N^2) overall.
Introduce a counter initialized to the total number of non-mine cells. Decrement it each time a safe cell is revealed, and when it reaches zero, the game is won.
Highlight that the counter approach gives O(1) win detection per move and O(1) extra space. Discuss edge cases like first move or auto-reveal of empty regions.
Relate the solution to Amazon's focus on customer obsession (fast, responsive game) and ownership (considering scalability and efficiency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't even think about this until it came up.
Start by clarifying the requirements and constraints, then propose a solution that guarantees a safe first click by relocating the mine or regenerating the board. Discuss trade-offs between different approaches and justify your choice based on user experience and technical feasibility.
Pro tip: Mention that this is a common UX pattern in Minesweeper and that the first click should always be safe to avoid frustrating players. Also, consider edge cases like when the board is too dense to relocate a mine.
Ask questions to understand the expected behavior: should the first click always be safe? Are there constraints on board size or mine density? This shows you think before coding.
Outline possible approaches: (a) relocate the mine to another cell, (b) regenerate the board until the first click is safe, (c) delay mine placement until after the first click. Discuss pros and cons of each.
Select the best approach based on trade-offs. For example, delaying mine placement is efficient and guarantees safety, while relocation might be simpler if mines are already placed.
Consider scenarios like when all other cells are mines (impossible to relocate) or when the board is very small. Explain how you would handle these gracefully.
Briefly describe how you would implement the chosen solution, including data structures and algorithms, and how it integrates with the rest of the game logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with an immutable snapshot of game state plus an append-only event log of reveal/flag/unflag actions.
Start by clarifying requirements: what game state needs to persist, how often it changes, and the consistency/latency trade-offs. Then propose a design that uses a cloud-based store (e.g., DynamoDB) with a well-defined data model, versioning for conflict resolution, and an API layer for sync. Finally, discuss how to handle offline play and multi-device conflicts, and how to scale the solution.
Pro tip: Emphasize idempotency and conflict resolution early—Amazon cares about correctness at scale. Also, mention that you'd start with a simple last-write-wins approach but be prepared to evolve to CRDTs or operational transforms if the game requires it.
Ask about the game type, state size, update frequency, offline support, and consistency needs. Determine if real-time sync is required or if eventual consistency suffices.
Define a schema for game state that supports versioning and partial updates. Consider using a document store like DynamoDB with a primary key of userId+gameId and a version attribute.
Design REST or WebSocket endpoints for saving and loading state. Include mechanisms for conflict detection (e.g., version checks) and resolution (e.g., last-write-wins, merge).
Describe how the client caches state locally and syncs when online. Explain conflict resolution strategies and how to handle merge conflicts for complex state.
Discuss partitioning, replication, and backup strategies. Mention how to ensure low latency and high availability across regions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward once the object model is clear.
Start by clarifying the scope: is this a REST API for a single-player game, or does it support multiplayer? Then define a minimal, resource-oriented API that covers game creation, move execution, and state retrieval. For each method, specify the HTTP verb, path, request body, and response structure, including status codes and error handling.
Pro tip: Emphasize idempotency and statelessness: design the API so that repeated identical requests (e.g., revealing a cell) produce the same result, and avoid server-side session state by using game IDs. This aligns with Amazon's leadership principles like 'Customer Obsession' and 'Ownership' by ensuring reliability and scalability.
Ask clarifying questions to determine if the API is for a single-player or multiplayer game, and whether it needs to support features like flags, timers, or leaderboards. This ensures you design the right surface.
Define the main resources: Game and Move. Determine the operations: create a game, make a move (reveal or flag), get game state, and possibly reset or delete a game.
Map operations to HTTP methods and paths. For example: POST /games to create, GET /games/{id} to retrieve state, POST /games/{id}/moves to reveal or flag a cell, and DELETE /games/{id} to end a game.
Specify the JSON structure for requests (e.g., move action, coordinates) and responses (e.g., game board, status, error messages). Include HTTP status codes for success and failure cases.
Explain how to handle invalid moves, game over conditions, and concurrency. Mention idempotency keys or versioning if needed to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.