AmazonIn the North American market, the competition is super fierce, and the success rate of Amazon SDE positions in 2025 is only 2-4%! Don't panic, although Amazon VO is strict, but if you are well-prepared, you will be able to come out on top! In this article, we will give you the linen interview experience and practical advice to help you rush to the offer with confidence!
Amazon VO Preparation
Amazon VO has 4-5 rounds of about 4 hours that test coding, system design, and behavioral performance. Technical posts have to brush up on LeetCode medium to difficult problems (arrays, trees, dynamic programming), practice with HackerRank and Chime (Amazon VO tool), try to talk about ideas while writing code. System design must understand high concurrency scenarios, such as order or recommendation system, study DynamoDB, SQS. behavioral questions to prepare 3-5 STAR stories, closely related to Amazon's 16 leadership principles, such as "customer first".
Amazon VO workflows
Resume ScreeningHR for my position prefers candidates with relevant experience in e-commerce, cloud services or distributed systems. We'll have a 30-minute phone call ahead of time to learn about the background and goals of the job search.
OA: Conducted on the HackerRank platform, it is 60 - 90 minutes in length and consists of 2 - 3 algorithmic questions, with the possibility of additional work style tests for some positions.
Technical phone interview: A 45-minute round of Amazon Chime whiteboard programming that requires candidates to answer algorithmic questions.
VOThe total number of rounds is 4-5. 2 rounds of coding interviews to test algorithmic and coding skills, 1 round of system design interviews to assess the level of system architecture design, and 1-2 rounds of behavioral interviews to ask questions based on leadership principles. Bar Raiser may be involved in the interview process to control the quality of the interview.
Algorithm + System Design + Behavioral Questions
This interview consisted of three main questions covering algorithms, system design, and Amazon style behavioral questions. I've included my own solutions or reference answers for each question for students preparing for tech positions.
I. Algorithm question: order prioritization
Title Description:
Given a list of orders, each containing an id, priority, and time of order placement, order sorting is required according to the following rules:
- First in order of priority from highest to lowest
- If the priority is the same, then the order will be placed in order of time, from early to late
The final output is a list of sorted order ids
Example:
[
{id: "o1", priority: 2, time: "2025-01-01"},
{id: "o2", priority: 2, time: "2025-01-02"}, {id: "o3", priority: 1, time: "2025-01-01"}, {id: "o3", priority: 1, time: "2025-01-01"}
{id: "o3", priority: 1, time: "2025-01-01"}
]
exports
["o1", "o2", "o3"]
Problem Solving Ideas:
Use Python's built-in sorted function, just customize the sorting rules. First, you can customize the sorting rules by pressing -priority row, then press time Ascending order. The overall complexity is O(n log n), the logic is clear, and it's not too long to write.
II. System Design Questions: Amazon Recommendation System
Title Description:
Design an e-commerce recommender system that is required to support billions of users and products with low latency and high relevance. For example, when users browse cell phones, to be able to recommend the corresponding accessories or similar phones.
My solution to the problem is as follows:
Architecture section:
- User behavior data stored in DynamoDB
- Product indexes are built with ElasticSearch
- Asynchronous processing of recommended tasks with SQS
- Top Picks Results Cached with Redis
Process Design:
User sends a request → API Gateway receives it → Recommendation service calls ES to get candidate products → query user's historical behavior → return recommendation list after comprehensive scoring
Summary of key points:
- For the recommendation algorithm layer I used collaborative filtering (item-based)
- System scalability by database sharding
- Latency control relies on caching and asynchronous queues
Time complexity: about O(log n) for queries, O(1) for cache hits
III. Behavioral questions: customer first
Title Description:
Amazon places a high value on putting the customer first, and this question asked me to share an experience where I took the initiative to solve a problem and improve the experience for a customer.
My reference answer (STAR):
Context:
The e-commerce page we were responsible for at the time was loading particularly slowly, and user feedback was already affecting the ordering experience.
Mission:
Locate problems and optimize performance as quickly as possible to improve customer satisfaction.
Action:
The first time I checked the logs, I found that the database query is particularly slow. After that, we optimized the SQL index structure and added a caching mechanism jointly with the team. At the same time, we did front-end lazy loading to improve the speed of the first screen.
Results:
The page load time was reduced by about 30%, and the positive feedback rate in the customer satisfaction survey was increased by 15%. This optimization was also kept on file by the team as a case study for technical sharing.
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