Round 1: DSA + Problem Solving (Technical Coding Round)
Duration: 60 minutes
Mode: Remote (Live Coding)
Difficulty: Medium
Questions Asked:
Search in Rotated Sorted Array
Print Top View of Binary Tree
Array-based problem (implementation-focused)
My Experience:
The round began with a quick self-introduction, followed by a classic DSA question —
"Search in a Rotated Sorted Array". I was initially stuck on some edge cases and needed a few hints to get back on track.
Once reminded about the possible edge conditions, I corrected my approach and completed the solution and Write a working code.
The interviewer then asked a conceptual tree-based problem: Print Top View of Binary Tree, where I was able to come up with a working pseudocode
after a few nudges.
The final question involved an array manipulation task, where I successfully wrote a complete working solution without much support.
Throughout the round, the focus was on logical thinking, debugging on the fly and edge case handling.
Verdict:
DSA-focused candidate with strong problem-solving skills. Needs to improve handling of edge cases independently and develop more confidence
under pressure.
Round 2: Project Deep Dive + System Thinking
Duration: 60 minutes
Mode: Remote
Difficulty: Medium
Discussion Topics:
Deep dive into Hackathon Project (AI + avatar lip-syncing)
Limitations, bottlenecks (RAM usage)
Scaling strategies and cost optimization
Caching strategies and implementation
My Experience:
We started by discussing my hackathon project, which involved facial analysis, AI-driven question answering
and lip-syncing with an avatar. The interviewer asked in-depth questions about the tech stack and implementation, especially around
performance and scalability.
I identified RAM as a bottleneck and proposed horizontal scaling. When asked about cost optimization
(e.g., handling idle servers at night), I mentioned shutting down machines but struggled to detail how to automate this — a clear learning point.
The discussion moved toward caching strategies. I correctly identified redundant computations and suggested a shared cache across servers.
However, I initially confused CPU L1 cache with application-level caching.
Eventually, I proposed using hashmaps and browser-based localStorage for client-side caching, especially when the same user accesses repeated data.
Despite some confusion and slow starts, I was able to think through the problems and reach practical solutions.
Verdict:
Shows strong project ownership and real-world reasoning. Needs to deepen understanding of system design concepts, cost optimization techniques, and caching architectures.
Round 3: System Design + Behavioral Insight
Duration: 60 minutes
Mode: Remote
Difficulty: Medium
Topics Covered:
Scaling and optimization
Handling concurrency
Edge case considerations
Behavioral Q&A (growth mindset, adaptability)
My Experience:
This round felt like a blend of system design discussion and behavioral analysis.
We discussed:
What happens if multiple users ask the same question?
→ I recognized the need for deduplication and caching.
How to cache at different layers?
→ I suggested using server-side hashmap cache and browser localStorage for repeated user access.
How to implement such a cache system?
→ I proposed a simple hashmap-based cache with key-value storage.
Behavioral Questions:
What if your solution doesn’t scale?
What would you do if your team disagrees on architecture?
While I didn’t have all the answers right away, I remained calm, clarified questions and reasoned my way through.
Verdict:
Logical and thoughtful problem-solver. Demonstrates growth potential and willingness to learn.
Would benefit from mentoring in scalable system design.
Round 1: DSA + Problem Solving (Technical Coding Round)
\n
\n
Duration: 60 minutes
\n
Mode: Remote (Live Coding)
\n
Difficulty: Medium
\n
\n
Questions Asked:
\n\n
Search in Rotated Sorted Array
\n
Print Top View of Binary Tree
\n
Array-based problem (implementation-focused)
\n\n
My Experience:
\n
The round began with a quick self-introduction, followed by a classic DSA question —\n\"Search in a Rotated Sorted Array\". I was initially stuck on some edge cases and needed a few hints to get back on track.\nOnce reminded about the possible edge conditions, I corrected my approach and completed the solution and Write a working code.
\n
The interviewer then asked a conceptual tree-based problem: Print Top View of Binary Tree, where I was able to come up with a working pseudocode\nafter a few nudges.
\n
The final question involved an array manipulation task, where I successfully wrote a complete working solution without much support.
\n
Throughout the round, the focus was on logical thinking, debugging on the fly and edge case handling.
\n
Verdict:
\n
DSA-focused candidate with strong problem-solving skills. Needs to improve handling of edge cases independently and develop more confidence\nunder pressure.
\n
Round 2: Project Deep Dive + System Thinking
\n
\n
Duration: 60 minutes
\n
Mode: Remote
\n
Difficulty: Medium
\n
\n
Discussion Topics:
\n
\n
Deep dive into Hackathon Project (AI + avatar lip-syncing)
\n
Limitations, bottlenecks (RAM usage)
\n
Scaling strategies and cost optimization
\n
Caching strategies and implementation
\n
\n
My Experience:
\n
We started by discussing my hackathon project, which involved facial analysis, AI-driven question answering\nand lip-syncing with an avatar. The interviewer asked in-depth questions about the tech stack and implementation, especially around\nperformance and scalability.
\n
I identified RAM as a bottleneck and proposed horizontal scaling. When asked about cost optimization\n(e.g., handling idle servers at night), I mentioned shutting down machines but struggled to detail how to automate this — a clear learning point.
\n
The discussion moved toward caching strategies. I correctly identified redundant computations and suggested a shared cache across servers.\nHowever, I initially confused CPU L1 cache with application-level caching.
\n
Eventually, I proposed using hashmaps and browser-based localStorage for client-side caching, especially when the same user accesses repeated data.
\n
Despite some confusion and slow starts, I was able to think through the problems and reach practical solutions.
\n
Verdict:
\n
Shows strong project ownership and real-world reasoning. Needs to deepen understanding of system design concepts, cost optimization techniques, and caching architectures.
\n
Round 3: System Design + Behavioral Insight
\n
\n
Duration: 60 minutes
\n
Mode: Remote
\n
Difficulty: Medium
\n
\n
Topics Covered:
\n
\n
Scaling and optimization
\n
Handling concurrency
\n
Edge case considerations
\n
Behavioral Q&A (growth mindset, adaptability)
\n
\n
My Experience:
\n
This round felt like a blend of system design discussion and behavioral analysis.
\n
We discussed:
\n
\n
\n
What happens if multiple users ask the same question? \n→ I recognized the need for deduplication and caching.
\n
\n
\n
How to cache at different layers? \n→ I suggested using server-side hashmap cache and browser localStorage for repeated user access.
\n
\n
\n
How to implement such a cache system? \n→ I proposed a simple hashmap-based cache with key-value storage.
\n
\n
\n
Behavioral Questions:
\n
\n
What if your solution doesn’t scale?
\n
What would you do if your team disagrees on architecture?
\n
\n
While I didn’t have all the answers right away, I remained calm, clarified questions and reasoned my way through.
\n
Verdict:
\n
Logical and thoughtful problem-solver. Demonstrates growth potential and willingness to learn.\nWould benefit from mentoring in scalable system design.
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