OpenAI Interview Questions

Megha V. - "Gradient Descent is an optimisation strategy used in several supervised learning models. It is the technique for finding the optimum solution of an objective function. Typically, for a linear regression use case, it is used to find the weights and bias that produce the lowest loss. It involves computing the partial derivative of the objective function with respect to the weight and bias vectors. To find the optima of the function, the derivative is equated to 0, and iteratively the weight and b"See full answer

Sachin P. - "The key here is a) not to overreact to Sales bypassing me going straight to Engineering (while understanding every team acts according to their incentives), and b) communicate with all parties as much as possible to get everyone on the same page. 1) First, I'd speak to Sales and understand the feature being requested. I consider Sales to be one of the most important primary research funnels - they have direct, regular touchpoints with the client and are finely attuned to the lifeblood o"See full answer

Snehangsu B. - "In a recent implementation of a large core banking project where we were breaking away from monolithic architecture to microservice based one we faced a situation where customer wanted to have a single repository of user and role management. Since Micro service based architecture talks about individual entities for multiple product suite it was mandatory to maintain individual user base. Based on the customer feedback, we had to come up with an unique design of single user base and replicating"See full answer

Darpan D. - "Clarifying questions and Assumptions ChatGPT search means the search function inside the chat app? OR ChatGPT search Chrome extension? Assumption: Search inside the chat app. Is there any location restriction in this analysis? Assumption: USA only. Is there any user segment restriction in this analysis? Assumption: All user segments. Are we assuming the ChatGPT search already exists or going back in time before the ChatGPT search existed? Assumption: Go back in time"See full answer

Anonymous Capybara - "using a relational database isn't a good choice for this system! we need more availability here than consistency (CAP theorem)"See full answer

JGJGJG - "What do you think you fumbled here with? how would you have restructured the answer with the interviewer? appreciate the reply here :)"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