DataSpoof

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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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