OpenAI Interview Questions

Scott S. - " Project Overview: Real-Time Risk Management System Objective The goal was to develop a real-time risk management system capable of processing and analyzing large volumes of trading data to provide near-instantaneous risk assessments. This system was crucial for enabling traders to make informed decisions while managing their exposure to various market risks in real-time. Complexity Factors 1. \\Data Volume and Velocity\\ \\High Throughput:\\ The system needed to ha"See full answer

Vaishnavi T. - "During my time as a Product Manager, we were preparing for a major product release with a tight deadline and discovered that implementing both a new feature and crucial performance improvements wasn't feasible within the timeframe. So, I organized a meeting with stakeholders to assess the impact of each option. We analyzed user data, projected ROI, and considered long-term product strategy. Ultimately, I decided to prioritize the performance improvements. As a result, the product launched on tim"See full answer

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

Tarun K. - "I would very confidently say - Never. Did I failed with a Product? - Yes. Did I launch a product that was sub-par? - Yes. Did I failed to launch a product? - Never. Also, I don't see it as part of growing up as PM to Fail to launch a Product. I think it's a tricky quesion, better to be avoided."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

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

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