I had actually prepped this exact scenario and felt good going in.
Use the STAR method to describe a specific instance where you proactively identified inefficiencies in an existing system, analyzed root causes, and implemented improvements with measurable impact. Emphasize your thought process, collaboration, and how you balanced improvement with maintaining system stability.
Pro tip: Quantify the before-and-after impact (e.g., reduced latency by 30%, saved 10 engineering hours per week) and highlight how you validated the improvement without disrupting ongoing operations.
Briefly describe the system/process, its purpose, and why it was considered 'functioning' but had underlying inefficiencies.
Explain how you discovered the issue—through metrics, user feedback, or personal observation—and why you decided to act.
Detail your investigation: data analysis, stakeholder interviews, or code profiling to pinpoint the root cause.
Describe the solution, how you rolled it out (e.g., A/B test, canary deployment), and how you measured success.
Quantify the impact and reflect on what you learned or how you shared the improvement with the team.
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 specific instance where you made a decision with incomplete data or tight deadlines. Highlight how you assessed risks, took action, and learned from the outcome, tying it to Amazon's Leadership Principles like Bias for Action and Deliver Results.
Pro tip: Emphasize the data you did have and how you used it to make a calculated decision, rather than focusing on what you lacked. Show that you can move forward without perfect information, a key trait at Amazon.
Briefly describe the situation, including the project, your role, and the constraints (e.g., limited data, time pressure).
Clearly state the difficult decision you had to make and why it was challenging given the circumstances.
Describe the steps you took to gather available information, assess risks, and make the decision. Highlight any trade-offs considered.
Explain the results of your decision, including any metrics or feedback, and whether it achieved the desired outcome.
Discuss what you learned from the experience and how it has improved your decision-making in ambiguous situations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was essentially a known hard leetcode problem with some surface-level rewording and a couple additions bolted on.
Start by clarifying the problem constraints and defining the objective function. Then propose a binary search on the answer combined with a greedy feasibility check, and discuss trade-offs with alternative approaches like dynamic programming or heuristics. Finally, analyze time and space complexity and consider edge cases.
Pro tip: Mention that this is the classic 'minimize maximum load' problem and that binary search on the answer is the optimal approach for many variants, but also acknowledge that for small inputs or specific constraints, other methods might be simpler. This shows you understand the problem deeply and can adapt.
Ask questions to understand constraints: number of workers, number of tasks, whether tasks can be split, if there are dependencies, and the range of costs. Confirm that the goal is to minimize the maximum total load assigned to any worker.
Formalize the problem: given tasks with costs, assign each task to a worker to minimize the maximum sum of costs per worker. Define a feasibility function: can we assign tasks such that no worker exceeds a given load L?
Use binary search on the answer L. For each candidate L, check feasibility using a greedy approach (e.g., first-fit decreasing) or a more exact method like dynamic programming if needed. Explain why binary search works: feasibility is monotonic.
Discuss time and space complexity of the proposed solution. Compare with alternatives like dynamic programming (pseudo-polynomial) or integer programming. Mention that greedy alone may not be optimal, but combined with binary search it yields the optimal for this problem when tasks are indivisible and workers are identical.
Consider edge cases: more workers than tasks, very large costs, or tasks that cannot be split. Discuss extensions like non-identical workers, task dependencies, or online scheduling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.