Recently helped a trainee review the Amazon SDE 的完整面试，整个过程非常有代表性。学员是 美国某 TOP30 本科 CS 应届生，刷题比较扎实，项目经历也还不错，但一开始对 亚麻 LP 问题的准备不足，有点担心回答太泛。我们提前帮他梳理了项目亮点，用 STAR 法打磨案例，最终顺利完成了三轮面试。
Amazon SDE 面试流程结构概览
亚麻 SDE 的整体面试流程通常为：简历筛选 → Online Assessment（OA）→ Virtual Onsite（VO）→ 招聘评估。需要注意的是，Amazon Leadership Principles 并不是只在行为面试中出现，而是从流程最早期就开始贯穿评估。
在简历筛选阶段，招聘方除了看技术背景和项目经历，也会关注候选人是否体现 Ownership、Deliver Results 等 LP 特质，例如是否有完整负责项目、推动问题闭环的经历。
OA 通常由两部分组成。第一部分是算法题，题型集中在数组、字符串、树、贪心和动态规划等高频考点，重点不仅是写出正确答案，还包括逻辑清晰度和边界处理能力。第二部分是行为或工作风格测试，这一模块与亚麻 LP 的关联度非常高，题目通过场景选择的方式，考察你在压力、冲突和不确定性下的决策倾向，往往会影响后续面试官对你的整体判断。
VO 一般包含 3–4 轮技术与行为交叉面试。Coding 轮在考算法的同时，会追问你的方案选择和权衡逻辑，用来映射相关的领导力原则；System Design 轮不仅关注架构能力，也会结合过往经历提问，考察长期思维和结果导向；最后通常会有一轮高度聚焦 Amazon Leadership Principles 的行为面试，对经历细节进行深入追问。
总体来说，能否系统性地将自己的经历与亚麻 LP 对齐，是通过整个面试流程的关键。
Round 1: Algorithmic Programming (Coding)
Topic I: Jump Game II (Minimum number of jumps)
The first reaction of the trainee was to use DP, thinking of recording the minimum number of steps for each position. However, after writing a few lines, they realized that the state transfer is too complicated, the time complexity is close to O(n²), and it will surely time out in the case of large arrays.
We reminded him to think differently: the key to each step is "where to jump to cover the farthest". So he adjusted to a greedy solution: two variables to maintain the current_end And farthestIn this case, it is a process of traversing the array, constantly updating the furthest reachable position, and jumping once it reaches the current boundary. This process is like the hierarchical traversal of BFS, which is clearer and meets the expectations of the interviewer.
After adjustments, the code was written in a few minutes, with a couple of edge cases along the way, such as when the array length is 1 or when the elements are 0.
Topic II: Binary Tree Cameras (Binary Tree Monitoring)
The difficulty with this problem is the state definition. The trainee only thinks of violent enumeration at first: each node either loads a camera or not, and then recursively counts the minimum number. However, once the recursive tree is expanded, the complexity directly explodes and it is not feasible.
We guided him to think, "Can we define states for each node to reduce double-counting?"
He later used DFS back-order traversal to give nodes in three cases:
- The node is fitted with a camera;
- The node is covered (child nodes have cameras);
- The node is not overwritten.
The traversal prioritizes the child nodes to take on the coverage responsibility and puts the camera at the current node only when necessary. With this turn of thought, it goes from exponential complexity to linear complexity. The interviewer nodded directly after writing it.
Round 2: Algorithmic + Behavioral Hybrid
Coding: Remove K Digits (Remove K Digits for Minimum)
When the trainee sees the question, his intuition is "violently remove all combinations and then take the minimum", but immediately realizes that this is exponential and not feasible at all.
He got stuck for a few seconds, and we hinted that for these "delete elements to make the result smaller" questions, the probability is to use a stack or greedy.
He tries to use a monotonically increasing stack: iterating over the numbers from left to right, and if the current number is smaller than the top of the stack, he keeps popping off the larger number, which ensures that the result is as small as possible.
After writing, we found a pitfall: if K is not used up, we have to continue to delete from the end; and the result has to remove the leading zero, otherwise there will be more cases like "0012". We reminded these two points in advance, and the students corrected them in time and passed successfully.
Behavioral：亚麻 LP 高频问题
At first, the participant's answer to the LP was a bit of a "running gag", for example, when he talked about "a project that he is proud of", he only mentioned "making an automated reporting system". We helped him expand the details: why (background pain points) → how to do it (specific program) → how to achieve the results (quantitative improvement), which is in line with the STAR framework and makes the story more convincing.
建议列表 + 模板示例：
| 领导力原则 | 典型问题 | 回答要点 |
|---|---|---|
| the customer reigns supreme | 请举例说明你如何为客户争取利益 | 展示如何优先考虑客户需求，并实际推动改进或产生影响 |
| 主动承担责任 | 请描述一次你主动承担超出职责的工作 | 展现主动承担风险或解决问题的经历，体现责任感 |
| 快速行动 | 请举例说明你在时间紧迫下做出的决策 | 强调在有限信息下快速做出合理决策，并承担后果 |
| 交付结果 | 请分享一次你在压力下完成关键目标的经历 | 聚焦最终结果和实际影响，体现执行力和影响力 |
FAQ - Amazon Software Development Engineer
Q1: Will the algorithm questions for Amazon SDE be cold?
A: No, most of them are LeetCode high-frequency questions, focusing on the clarity of ideas and the ability to write bug-free code quickly.
Q2: How to prepare for LP behavioral questions?
A: It is recommended to prepare 6~8 STAR cases in advance, covering common scenarios (conflict resolution, efficiency improvement, customer focus, leadership).
Q3: What should I do if the Coding session gets stuck?
A: Dare to exchange ideas with the interviewer, do not be deadlocked. Linen values "communication + iteration", not just the final answer.
Q4: Is there a gap between the three rounds of interviews?
A: Usually about a week, at a tight pace.
Contact us now and stop fighting alone!
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