This came from the first round with an SDE and a kernel engineer, so the bar felt pretty specific.
Start by clarifying requirements and constraints (e.g., scale, consistency, durability) to scope the design. Then outline a high-level architecture, diving into core components like metadata management, data storage, and caching. Finally, discuss trade-offs and justify your choices based on Amazon's leadership principles.
Pro tip: Demonstrate customer obsession by tying design decisions back to user needs and business impact. Also, proactively discuss how you would handle failures and ensure high availability, as Amazon values operational excellence.
Ask questions to understand the expected scale (number of users, files, size), consistency needs, durability, latency, and any specific features like versioning or sharing.
Sketch the main components: client, API gateway, metadata service, block storage, and caching layer. Explain how they interact.
Elaborate on metadata storage (e.g., distributed database), data partitioning, replication for durability, and consistency models.
Discuss how to scale horizontally, use caching (e.g., Redis), CDN for hot data, and optimize for read/write patterns.
Compare design choices (e.g., SQL vs NoSQL for metadata, strong vs eventual consistency) and justify your decisions based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
These showed up in every single round, sometimes 5 questions, sometimes 20+.
Use the STAR method to structure each answer, ensuring you clearly describe the Situation, Task, Action, and Result. Focus on demonstrating Amazon's Leadership Principles, especially those related to adaptability, stakeholder management, and conflict resolution. Tailor your examples to show how you navigated ambiguity, influenced stakeholders, and resolved conflicts to deliver results.
Pro tip: Quantify your results whenever possible and explicitly tie your actions back to specific Amazon Leadership Principles. Show self-awareness by briefly mentioning what you learned or would do differently, demonstrating growth and maturity.
Set the context by describing a specific situation you faced, including the challenges related to adaptability, stakeholders, or conflict. Be concise but provide enough detail to make the scenario clear.
Explain your responsibility in that situation. What were you tasked with achieving? What were the constraints or expectations?
Detail the actions you took to address the situation. Focus on your specific contributions, decisions, and how you applied relevant Leadership Principles. Highlight how you navigated ambiguity, managed stakeholders, or resolved conflicts.
Describe the outcomes of your actions. Quantify the impact if possible (e.g., time saved, revenue increased, customer satisfaction improved). Explain how your actions benefited the team, project, or company.
Reflect on what you learned from the experience and how it has influenced your subsequent work. This shows self-awareness and a commitment to continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did not see this coming from a Director-level interviewer.
Start by clarifying the problem and constraints, then define the state and recurrence relation before coding. Walk through a small example to validate your DP, and discuss time/space complexity and possible optimizations like rolling arrays.
Pro tip: At Amazon, emphasize how your DP solution scales and consider edge cases like empty inputs or large values. Mention trade-offs between top-down and bottom-up approaches, and how you'd test your solution.
Ask questions to understand input/output, constraints, and edge cases. Confirm whether you need the optimal value or the actual solution.
Identify what each state represents and what parameters are needed. Explain why this state captures the subproblem.
Formulate how to compute a state from smaller states. Discuss base cases and initialization.
Decide between top-down (memoization) and bottom-up (tabulation). Justify your choice based on constraints and clarity.
State time and space complexity. Suggest optimizations like reducing dimensions or using rolling arrays if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem (e.g., intervals are closed, input may be unsorted) and discuss edge cases. Then present the optimal sort-then-merge approach: sort intervals by start time, iterate and merge overlapping intervals. Analyze time and space complexity, and mention alternatives like sweep line if relevant.
Pro tip: At Amazon, emphasize how your solution scales with large inputs and how you would handle real-world constraints like streaming data or memory limits. Also, proactively discuss how you would test the solution with edge cases.
Ask questions to confirm interval representation (inclusive/exclusive), input size, whether intervals are sorted, and expected output format. This shows attention to detail and avoids assumptions.
Mention cases like empty input, single interval, non-overlapping intervals, fully overlapping intervals, and intervals that touch at endpoints. This demonstrates thoroughness.
Explain the sort-then-merge algorithm: sort intervals by start time, then iterate and merge if the current interval overlaps with the last merged interval. Walk through a small example.
State that sorting takes O(n log n) time and merging takes O(n) time, so overall O(n log n) time. Space complexity is O(n) for the output (or O(log n) if sorting in-place, depending on language).
Outline how you would test the solution with unit tests covering edge cases. Optionally, mention extensions like merging intervals in a stream or with additional constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: given coin denominations and a target amount, find the minimum number of coins needed (or the number of ways, depending on the variant). Then explain the dynamic programming approach, defining the state (e.g., dp[i] = min coins for amount i), the recurrence, and the base case. Finally, discuss time and space complexity and possible optimizations.
Pro tip: At Amazon, interviewers value candidates who proactively discuss trade-offs and edge cases, so after presenting the DP solution, mention how you would handle impossible amounts (return -1) and consider if greedy works for special cases (e.g., canonical coin systems).
Ask whether the goal is to minimize the number of coins or count the number of ways, and confirm if coins can be reused (unlimited supply). Also check constraints on amount and coin denominations.
For minimum coins: let dp[i] be the minimum coins to make amount i. Initialize dp[0]=0 and dp[i]=infinity for i>0. For each coin c, update dp[i] = min(dp[i], dp[i-c]+1) for i from c to amount.
Trace the algorithm on a small example (e.g., coins=[1,2,5], amount=11) to demonstrate correctness and build intuition.
State time complexity O(amount * number of coins) and space O(amount). Discuss edge cases: amount=0, no solution, large amount, and coin denominations including 0 or negative (invalid).
Mention space optimization (1D array), BFS approach for minimum coins, and when greedy works (e.g., canonical systems). Also note that for counting ways, the recurrence changes to dp[i] += dp[i-c].
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.