Amazon VO | Summary of Amazon interview questions | Analysis of high-frequency Amazon Leetcode question types​

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Amazon The interview is not easy, and it is not the type that can be solved by answering a few questions. It does not pursue "fancy solutions", but it has very solid basic requirements, and especially likes to test some above-average Leetcode questions. In this article, I have compiled the overall process of Amazon VO, classification of high-frequency question types, explanations of real questions, and behavioral tips. They are all summarized from actual practice with students. I hope it can help you who are rushing to apply for Amazon or other major companies.

Amazon VO | Summary of Amazon interview questions | Analysis of high-frequency Amazon Leetcode question types​

Amazon interview overall process

According to 2025 data from Glassdoor and Levels.fyi, the interview process for Amazon technical positions (especially SDEs) typically includes:

  • Online Assessment (OA): 1-3 rounds of LeetCode style coding questions + work style survey.
  • Phone Screen: 1 round of coding + Behavioral.
  • Virtual Onsite (Loop): 4-6 virtual interview rounds (usually completed in one day, or spread over two days), including Coding, System Design (intermediate-high level), Behavioral, and Bar Raiser rounds.
  • Decision time: Usually feedback within 5 working days.

Trends in 2025: Virtual interviews will be the main focus, the Bar Raiser process will continue to be strengthened (to ensure new employees “Raise the Bar”), Behavioral throughout the entire process, and strictly based on the 16 Leadership Principles (LP) evaluation.

Stage Number of rounds/duration Key inspections Common forms
Online Assessment 1-3 hours Algorithms, data structures, working styles HackerRank/Amazon Platform
Phone Screen 1 round 60 minutes Coding + LP Behavioral Virtual screen sharing
Virtual Onsite 4-6 rounds x 45-60 minutes Coding, System Design, Deep LP, Bar Raiser Virtual video

1. Hiring Manager Chat

  1. Regular Behavioral Interviews + Experience Digging + Team Introductions + Culture Fit Examination
  2. Be prepared to talk about past projects, how you solved problems, and why you're interested in Stripe.
Question Goal principle
Tell me about a time you failed and what you learned Learn & Be Curious, Earn Trust
Tell me about a time you disagreed with your manager Have Backbone; Disagree & Commit
Tell me about delivering with tight deadlines Deliver Results
Describe how you improved customer experience Customer Obsession

2. Code Question 1

Core: a simplified version of the "minimum number of trades balanced account" on LeetCode.
Task: Devise a solution (not necessarily optimal) to adjust all account balances to their respective target values.
Follow-up 1: How to realize minimal transaction, just answer along the lines of the original LC question (graph theory/greedy).
Follow-up 2: How to Audit transactions? Solution: Do a Dry Run to simulate all transactions and compare the results with the actual database state to verify consistency. [Focus on verifiability].

3. Code Question 2

Given a directed graph conns: the connectivity between different clusters in a distributed system: conns[i] = [a, b] indicates that cluster a has access to cluster b. Given a starting point source and an ending point dest, determine if all paths from source must end in dest.

4. Code Question 3

Given an integer list and integer limit, find a subarray (consecutive) such that the difference between the maximum and minimum values in this subarray is less than or equal to limit, and return the maximum length of this subarray.

Offers don't depend on luck, they depend on preparation!

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This article is compiled based on the author’s interview coaching experience, anonymous feedback from multiple 2024-2025 candidates, and recent data from public platforms (such as Glassdoor, Levels.fyi, TeamBlind). The Amazon interview process varies by position, level, team, and year (for example, SDE positions usually have OA, and Applied Scientists may focus more on ML knowledge). This article is mainly for software development engineers (SDE) and related technical positions and is for reference only.

author avatar
Jack Xu MLE | Microsoft Artificial Intelligence Technician
Ph.D. From Princeton University. He lives overseas and has worked in many major companies such as Google and Apple. The deep learning NLP direction has multiple SCI papers, and the machine learning direction has a Github Thousand Star⭐️ project.
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