Summary
I successfully secured a Development Intern (Cloud Computing) position at ByteSimplified, completing a multi-stage interview process that included a HackerRank screening, a hands-on cloud development task, a foundational computer science deep dive, and a final HR discussion.
Full Experience
I applied for the ByteSimplified Development Intern (Cloud Computing) role through Naukri. I'm a final-year student from a Tier 2 college, and this was for a 6-month remote internship.
Stage 1: HackerRank Screening
The first hurdle was a HackerRank screening round. It consisted of two easy-level LeetCode-style questions, and I had 30 minutes to solve them. Upon successfully clearing this round, I was shortlisted for the subsequent three interviews.Round 1: Technical – Cloud Application Development (LLM-Assisted)
This round lasted between 60 to 75 minutes and was quite practical. The interviewer tested my reasoning and ability to use cloud tools and frameworks. What made it interesting was that I was allowed to use an LLM like ChatGPT to assist me.The main problem statement was to design a mini “Search-as-a-Service” prototype. The requirements were specific:
- Build a lightweight search API using Flask or FastAPI with /upload and /search endpoints.
- Implement an in-memory index (like Whoosh or TF-IDF).
- Deploy it on AWS or Azure using a managed service such as Elastic Beanstalk or App Service.
- Include proper logging and error handling.
Round 2: Technical – Foundational Computer Science
This round was a deep dive into core computer science concepts. It comprised 10 to 15 conceptual questions spanning Algorithms, Data Structures, Operating Systems, Computer Networks, and Databases.Some of the questions asked included:
Algorithms & Data Structures
- How would you design a system to find the median of a continuously updating stream?
- What data structure would be most suitable for implementing an “undo” feature in an editor, and why?
Operating Systems
- When a new browser tab is opened, what OS resources are typically created?
- What are the primary causes of deadlocks, and can an operating system prevent them completely?
- Can you describe a scenario where caching might lead to a program performing slower rather than faster?
Computer Networks
- Why might a video call application choose UDP over TCP, even if it means sacrificing some reliability?
- When my browser displays “Secure” in the address bar, what technical chain of trust guarantees that label?
Databases
- Explain how a database index enhances query speed but could potentially degrade the performance of bulk inserts.
- When multiple transactions try to update the same record concurrently, what mechanisms ensure data integrity and prevent corruption?
- How would you design a database to efficiently store time-series data, such as readings from IoT sensors?
Round 3: HR and Fitment Discussion
The final round was a 30-minute conversation with HR. We discussed my projects, my technical interests, and my career goals. It was a good discussion, primarily assessing my alignment with the company and my overall motivation.I was ultimately selected for the internship position.
Interview Questions (11)
Design a mini “Search-as-a-Service” prototype with the following requirements:
- Build a lightweight search API using Flask or FastAPI with /upload and /search endpoints.
- Implement an in-memory index (Whoosh or TF-IDF).
- Deploy on AWS or Azure using any managed service such as Elastic Beanstalk or App Service.
- Add logging and error handling.
You need to find the median of a continuously updating stream — how would you design for that?
What kind of data structure would you use to implement an “undo” feature in an editor, and why?
When you open a new browser tab, what OS resources are created under the hood?
Why do deadlocks occur, and can the OS completely prevent them?
In what scenario could caching make your program slower instead of faster?
Why might a video call prefer UDP over TCP, even at the cost of reliability?
When your browser shows “Secure” in the address bar, what technical chain of trust ensures that label?
How does an index improve query speed but potentially hurt bulk inserts?
When two transactions update the same record, what ensures you don’t end up with corrupted data?
How would you design a database to store time-series data like IoT sensor readings efficiently?