Interview Questions

S S. - "Seamless user experience Consistent UI Data mapping Technical architecture of the 2 platforms and their compatibility API integrations Security and compliance factors to consider, impacting from the integration. Feature parity — to carry over features, enhance features, drop features etc Load testing, performance testing and end-to-end testing post integration"See full answer

Sandhya N. - "Clarifying Questions: Discover events, purchase/resell via app Post booking: After ticket purchase to event completion Motivation for Ticketmaster: Strengths: 70-80% of ticketing business, tie ups with venues Competition: meetup Low frequency product - so every experience matters Positive wom: Reduced marketing Retention of users, organizers Users: Ecosystem Attendees: Discover, buy and attend events - {chose this} Event Organizers: marketing/demand, s"See full answer

Anonymous Caribou - "Sales and Delivery app: Ask Clarifying questions: What all parts of the user journey does this app play a role in? All delivery notifications go through this app. Tesla while completing sales does ask its customer to download this app. Sales can be completely done through the app. Sales person at Tesla could be reached through app. App can be used to buy other peripherals for Tesla. Success Metrics: Revenue: \# of car sales completed through the delivery app. \# of peripheral"See full answer

Ishan S. - "I can see that Gen Z (born between 1995 and 2009) needs a redesigned washing machine with a busier life than ever, newer living arrangements, and a longer time frame. Let's try and understand why we need a new product for Gen Z: Clarifying question - 1) Do we have a specific goal while redesigning the machine? Should we optimize for speed, space, or functionalities? (Assume - No) 2) Do we have geographical constraints? Countries have different power and electrical thresholds, which can decide"See full answer

Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer

Rahul M. - "We should define automated Scheduled Updates for deployment to keep the application up to date."See full answer

Jake M. - "Random Forest is a machine learning model used for classification problems or regression problems. It can handle binary classification as well as multi-class classification. It is a very efficient model and is great for a baseline or used in a service that needs extremely low latency depending on the size of the model. It's also a good option for wide datasets (dataset with many features) due to it's random subset of features. it is slightly less optimized for deep datasets on very large dataset"See full answer