Amazon SDE Internship Interview Experience | OA + Technical Rounds | Rejected

Amazon SDE Internship Interview Experience | OA + Technical Rounds | Rejected

Hi everyone!

I wanted to share my Amazon interview experience from around five months ago. Although I didn't make it through the process, I hope this experience helps others preparing for Amazon interviews.

Round 1: Online Assessment (OA)

The OA consisted of two DSA coding problems and nine sections of behavioral questions based on Amazon's Leadership Principles.

My suggestion is to practice DSA consistently and prepare behavioral answers using the STAR method (Situation, Task, Action, Result).

Round 2: Technical Interview

The interview covered DSA, project discussion, and behavioral questions.

DSA Problem 1: Minimum Window Substring

Given two strings, find the smallest substring of the first string that contains all the characters required by the second string, including their frequencies.

Example:

• String 1: "timetopractice"
• String 2: "toc"
• Output: "toprac"

Approach: Sliding window with a frequency map.

DSA Problem 2: Rearrange Characters with a Minimum Distance

Given a character array containing lowercase English letters and an integer k, rearrange the characters so that identical characters are separated by at least k positions. If necessary, insert placeholder positions between characters and determine the minimum length of the resulting sequence.

Example 1:

• Characters: ['a', 'b', 'a', 'a', 'a', 'b']
• k = 3
• Minimum length: 13

Example 2:

• Characters: ['a', 'b', 'a', 'a', 'c', 'd']
• k = 4
• Minimum length: 11

Approach: Frequency counting and greedy placement, prioritizing the most frequent characters.

Round 3: Technical Interview

Project Discussion: Poultry Disease Detection

A significant part of the interview focused on my Poultry Disease Detection project, which uses deep learning and image classification.

The discussion lasted approximately 20 minutes.

Questions included:

• Explain your project and its architecture.
• How did you choose the model?
• Why did you select ResNet50?
• What is transfer learning, and how did you use it?
• What challenges did you face during development?
• How did you evaluate and finalize the model?
• Which AI tools have you used during development, and why?
• What limitations or incorrect suggestions did you encounter when using AI tools?
• How did you verify the correctness of AI-generated suggestions?
• Describe a difficult situation you faced and how you handled it.

My takeaway: Be prepared to explain every major technical decision in your project, including the alternatives you considered, evaluation metrics, limitations, and what you personally implemented.

This round included project-related questions and two DSA problems.

DSA Problem 1: Four Numbers Equal to a Target

Given an integer array and a target value, find four elements whose sum equals the target.

Example:

• Array: [4, 7, 12, 1, 6, 5, 2]
• Target: 16
• One valid output: [7, 1, 6, 2]

Approach: Sorting combined with two pointers, or hashing-based approaches.

DSA Problem 2: Maximum Candies per Child

Given an array representing candy piles and an integer k, determine the maximum number of candies each of the k children can receive. Each child must receive candies from a single pile, and piles can be divided but not merged.

Approach: Binary search on the answer. For a proposed number x, calculate whether the piles can provide at least k portions of size x.

Final Outcome

Unfortunately, I was rejected after the interview process.

Even though the outcome wasn't what I hoped for, the experience taught me the importance of strong DSA fundamentals, clear communication, detailed project knowledge, and preparation for behavioral questions.

A few lessons I took away:

• Practice explaining the reasoning behind your approach, not just writing code.
• Know the time and space complexities of your solutions.
• Understand your projects deeply, especially model selection, architecture, evaluation, and limitations.
• Prepare behavioral examples aligned with Amazon's Leadership Principles.
• Be honest about your mistakes, challenges, and the lessons you learned.

Sharing this in the hope that it helps someone preparing for Amazon.