Amazon Winter Internship — On-Campus Interview Experience

1. Company and Role

Type: Coding, Debugging, and MCQs
Description:
The online assessment consisted of three sections:
1. DSA Questions: Easy-to-medium-level coding questions that tested problem-solving and implementation skills.
2. Debugging Questions: I chose Node.js from the available options, which included C++ and Node.js. An AI assistant was provided to help with the debugging section.
3. Leadership Principles MCQs: Multiple-choice questions based on Amazon's Leadership Principles and workplace scenarios.
Difficulty Level: Easy to Medium
Experience:
The OA tested not only coding skills but also debugging ability and familiarity with Amazon's Leadership Principles. I would recommend practising DSA problems, getting comfortable with debugging code, and understanding the Leadership Principles before attempting the assessment.

Type: DSA and Behavioral Interview
Description:
The interview began with introductions from both the interview panel and me. The discussion then moved to two medium-level DSA problems.
The problems had to be solved using pen and paper. Along with writing the solution, I was expected to explain the approach, discuss its time and space complexity, and optimize the solution based on the interviewer's feedback and follow-up questions.
The interview also included behavioral questions based on Amazon's Leadership Principles.
Example behavioral question:
- Tell me about a time you had a deadline. How did you handle it?
The interview concluded with an opportunity to ask questions to the interviewer.
Difficulty Level: Medium
Experience:
The round emphasized structured problem-solving and communication. It was important to clarify the problem, explain the approach before coding, analyse complexity, and be prepared to improve the solution when given suggestions.

Type: DSA and Behavioral Interview
Description:
The second interview followed a similar format to the first one.
1. Introduction and brief discussion about my background.
2. Two medium-level DSA questions to solve on paper.
3. Explanation of the approach, time and space complexity, and optimization.
4. Behavioral questions related to Amazon's Leadership Principles.
5. Questions related to Generative AI.
6. An opportunity to ask questions to the interviewer.
The interviewers focused on how I approached problems, explained my reasoning, and responded to follow-up questions and suggestions.
Difficulty Level: Medium
Experience:
One of my key takeaways was that reaching the correct answer is only part of the process. It is equally important to demonstrate clear reasoning, ask relevant questions, understand the constraints, and communicate effectively throughout the problem-solving process.

Overall Interview Experience:
The overall process was a valuable learning experience. The interviews focused on DSA, optimization, communication, and behavioral aspects aligned with Amazon's Leadership Principles.
I felt that the interviewers were interested not just in the final solution, but also in understanding how I approached a problem, clarified requirements, evaluated trade-offs, and incorporated feedback.
Tips for Aspirants:
1. Clarify the problem statement and constraints before starting your solution.
2. Ask the interviewer relevant questions instead of making assumptions.
3. Explain your thought process step by step.
4. Always analyse the time and space complexity of your solution.
5. Look for optimization opportunities and be open to hints or feedback.
6. Prepare specific, genuine examples for behavioral questions based on your academic projects, internships, teamwork, and deadlines.
7. Practise writing code on paper, since both technical interviews in my experience followed this format.
Verdict: Selected