System Design Interview Questions

Rahul .. - "''' Problem Statement: Design a simplified Robinhood-like stock trading platform. Requirements: User Management: createuser(userid, name, initial_balance): Create user with cash balance getbalance(userid): Get user's cash balance deposit(user_id, amount): Add funds to account withdraw(user_id, amount): Withdraw funds (if sufficient balance) Stock Operations: updatestockprice(symbol, price): Update current price of a stock getstockprice(symb"See full answer

Robert H. - "The addition of an intermediate "sanitization" ML is something Neeraj used in the Uber Eats design and again seems kind of outside the scope here and redundant. This can simply be built into the AI response model to save a step. It's not clear what benefit this step provides, and he basically said we should have it "just because it would be good" and there's no concrete reasoning why to include it. Adding a Kafka queue to handle the thumbs-down ratings? For what purpose do you need a queue othe"See full answer

Harshit G. - "Great. I will start by understanding the goal of Google Photos and how it aligns with the goal and mission of Google. After defining goals, I will talk about the user actions which will contribute towards this goal. Based on these actions, we will define metrics. Check-in with the interviewer on the approach here. Assuming that this looks good for the interviewer to proceed. Google Photo - Helping users organise & manage their pictures. Completely in line with Google's mission. Thinking ab"See full answer

Nishant S. - "Missed the most important aspect in this case, how the user location is mapped to sensor data for nearest location, also how do we calculate weather for a city, do we aggregate and persist the data of all sensors in the city. What happen when few sensor dont report the data, do we provide the data from other nearby sensors and display a message related to accuracy. It was okay, pretty standard design which missed talking about critical aspects. Thanks for the session."See full answer

Anubhav S. - " At low level: I would use two stacks: one for forward history and other for backward history. i go to tryexponent.com => this url will be stored in backward history stack. i go to google => again this url will be stored in backward history stack. i press back => data from backward history will be popped and put in to forward history stack. I press forward => data from forward history stack will be popped and put in to backward history tab. Also, whenever i go to any url,"See full answer

Kapil D. - "Overall a very good summary of ranking problems. I wish there are more details in the post on depth of modeling. One can talk more about advance ML architectures like DCN, BertRec etc"See full answer

Shivastuti K. - "For somebody who needs accessibility accommodation, this doesn't have a transcript so it is not useful to me."See full answer

S R. - "Wrote up a simple cache system using python dict. Added TTL requirement. Then went into code-level concurrency issues for the cache."See full answer

Eshan P. - "Background A. Objective Lyft has a presence in Toledo, Ohio. At our current revenue per ride of $6, we can match 60% consumers requesting a ride with a driver. Our goal is to maximize net revenues in the next 12 months by figuring out the optimal revenue per ride. B. TLDR Summary · With a target market of (~100K-138K) consumers, on a $25 charge to consumers, Lyft should pay $20.75 to drivers and fix its share at $4.25 to maximize its net revenues over a 12-mont"See full answer

Sai vuppalapati M. - "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"See full answer

Teja G. - "To design a scalable ticketing system for peak events like Black Friday, you must employ a microservices architecture, implement robust load balancing, leverage caching, and use a combination of NoSQL and relational databases for different data types. Key features also include real-time seat booking, secure payment integration, mobile compatibility with QR code validation, robust admin dashboards for analytics, and integrated marketing tools like promo codes. The system needs to prioritize avail"See full answer

Aymen D. - "It would have been more interesting to focus on the system design rather than the Trie DS, Interviewee could have just mentioned the Trie and passed to things more important. Interviewee should have focused on the factors on which he wants to scale the API servers, popularity of the query parts ? region may be ? A hash of many factors ? Caches should have definitely be discussed, Cache eviction policies, Cache invalidation managements... Interviewee should have mentioned which kind of API pro"See full answer

Yashasvi C. - "I would first like to clarify the requirements and assumptions for the system. I am assuming that the system should support basic file operations such as uploading, downloading, deleting, renaming files, and viewing directory structures along with file permissions. The file size can range from around 1MB to 1GB, and the system is expected to be read heavy, meaning downloads will be more frequent than uploads. The system should support a large scale, for example around 10 million total users with"See full answer

Dinar M. - " Thanks a lot for showing us how a recommender system can be build. I see it was proposed to use Collaborative filtering which is user - item matrix having dimension N * M (where N - number os users and M - number of songs). Though, it was explained how it gonna be built, it is still unclear how all users and songs features are going to be used. In that matrix we have values in cell (lets say i, j) like 1 - a specific user (i) clicked on song (j) when it was recommended or it is 0 when the user"See full answer

Michael F. - "Prompt: We work for an online shopping website. Our team wants to consider offering discounts (e.g. 10% off your next purchase) to customers to incentivize them to make purchases. How would you design a system that decides how to offer these incentives? Answer Goals: Increase customer engagement while controlling costs. Specifically, we want the increase in revenue per customer per week of customers that receive the discount to be greater than the cost of the discount. Metrics: Revenue per cu"See full answer