OpenAI Interview Questions

Yash G. - "import lombok.*; import org.springframework.boot.CommandLineRunner; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.stereotype.Component; import org.springframework.stereotype.Repository; import org.springframework.stereotype.Service; import java.time.LocalDateTime; import java.util.*; @SpringBootApplication public class LldPracticeApplication { public static void main(String[] args) t"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

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

Basil A. - " import java.util.*; class Solution { static int leastInterval(char[] tasks, int n) { // your code goes here if(n == 0) { return tasks.length; } int[] freq = new int[26]; for(char c : tasks) { freq[c-'A']++; } int count = 0; Arrays.sort(freq); int len = tasks.length; int idx = 1; for(int i = 25; i >= 0 && freq[i] > 0; i--, idx++) { len = Math.max(len,"See full answer

Louisa C. - "I think AI such as ChatGPT seemed to spit out its answers fast and instinctively, hence proving its efficiency but in the recent versions it seems to have acquired reasoning skills which was revealed 'transparently' to users. This new, reasoning skills introduce a small delay in its response and explains what it does during the delay but I do not really think that it is "thinking" any differently than before, except that it is constrained by some purported rules when asked to summarise certain b"See full answer

Surbhi G. - "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt. A co"See full answer

Farza S. - "It is the smallest level of significance at which the null hypothesis gets rejected"See full answer

Constantin P. - "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"See full answer

Vibhor R. - "As a product manager, my focus is to bring the what and why of the product and deliver the product in the promised timeline. With that said, I typically work closely with my engineering and onboard them during the planning sessions to have a say on the design and feasibility. I try to keep an idea where PMs are not the only responsible folks for ideation and engineering is free to pitch in their two cents. In addition, I typically try to do daily/weekly standup check-ins with my Developers and D"See full answer

Hadrien M. - "Walk through user need and discovery, GTM process, beta planning, execution and measurement, results and learnings"See full answer