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My Amazon SDE Interview experience for the reference of all freshers applying: (FYI, Amazon just dropped their SDE-1 India University Graduate openings!) ⏳ The process: 1️⃣ 1 Online Assessment 2️⃣ 2 Coding rounds 3️⃣ 1 Coding + Leadership Principles round 💻 The interviews: 1️⃣ OA round: 7 basic code-debugging MCQs 2 DSA questions: - LC 2265. Nodes Equal to Average of Subtree - LC 68. Text Justification 1 (very lengthy) behavioral question form - Solved all 7 debugging questions correctly. - Solved first DSA problem in 10 mins. - Partially solved second problem, failing few test cases. - This round went average. But got the interview invite. Being fast in contests and debugging would help in this round. 2️⃣ Coding round 1: A BFS-based LeetCode hard problem. - Quickly coded a BFS + hashmap solution. - Interviewer had cross-questions but appeared satisfied overall. If you can solve LC 127. Word Ladder, you’d be fine. 3️⃣ Coding round 2: > Q1: Nodes at distance K in a binary tree - Used BFS after creating parent pointers using HashMap. > Q2: Connect ropes with minimum cost - Implemented a greedy solution using a priority queue. - Interviewer liked my speed but gave another problem. > Q3: Max steps with reduced m - Gave O(n) solution, then optimized using binary search to O(log n) and later to O(log(sqrt(m))). Overall, pleasant interview with optimized solutions. All of the above problems: - LC 863. All Nodes Distance K in Binary Tree - GFG. Connect n ropes with minimum cost - Problem 3 not on the internet. Here’s a playground for it - https://lnkd.in/gsg2Pnmp 4️⃣ Coding + Managerial round: - LC Hard; Smallest substring in ’s’ containing ’t’ as subsequence - Came up with a sliding window approach. - Took 30+ min to explain and code the approach. - Interviewer was satisfied with my approach, but couldn’t finish coding completely. - Overall, explained the concept but could have implemented faster. If you have done LC 76. Minimum Window Substring, you got this one. Behavioral Questions: [1] Internship Discussion: - Day-to-day responsibilities? - Technologies you worked with, and why? - Any accomplishments or key learnings? [2] Amazon Leadership Principles: - Time when you went above and beyond to meet a customer’s needs? (Customer Obsession) - Time when you had to make a quick decision with limited information? (Bias for Action) Decent answers in behavioral round as I had prepped for similar questions. 🎯 Result: My interview result was positive and a few weeks later, I got the life-altering SDE-1 offer from Amazon Credit- Harshit sharma

Overfitting happens when a model learns too much detail from training data, including noise, rather than general patterns. Result: The model performs well on training data but poorly on new, unseen data. Symptoms: High accuracy on training data, low accuracy on test data. Cause: Model is too complex (e.g., too many layers, features, or parameters). Example: Memorizing answers for a specific test rather than understanding concepts. Solution: Simplify the model, use regularization techniques, or gather more data. Purpose of Avoiding Overfitting: Ensures the model can generalize and make accurate predictions on new data.

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