Interview Questions

Going P. - "Customers needs changing every"See full answer

Abel alejandro H. - "I asked for clairication on what region in the world we were focusing on and I settled on the United States. I asked for clarification on the type of doctors and for what kind of patients and that was left up to me. I landed on any patient looking to find a doctor for "Preventive Medicine" given that the US is hurting on that. The pain points were around expensive/payment methods, access & lacking knowledge to know what kind of doctor is best for each person. I spent too much time here and had l"See full answer

Saurabh J. - "The algorithm calculates certain metrics like entropy & Gini Impurity. The goal of the decision tree algorithm is to find the most optimal value for these metrics, lowest values for Gini Impurity & Entropy. Once it converges on the minima, it creates a split & grows the branches."See full answer

Vishal T. - "Go has simpler syntax than Java. It is light weight. It is not Object Oriented. It does not support function overloading and function overriding. But these are small technical differences. Both are similar when it comes to testing. You have to create a mock object and implement an interface. Functionally, I did not feel any major difference."See full answer

Anonymous Chickadee - "was there a specific scenario given for the interaction?"See full answer

Exponent - "This is a Fermi problem — an estimation or approximation problem with limited information and back-of-the-envelope calculations. There's no right answer: interviewers want to understand how you think and how well you can explain your reasoning, rather than what you already know. Recall the formula for Fermi problems: Ask clarifying questions Catalog what you know Make equation(s) Think about edge cases to add to equation **Breakdown components of your equat"See full answer

Ayush kumar S. - "I submitted a PRD as it was a take home assignment."See full answer

Exponent - "This is a Measure Success question with a slight twist. The twist here is we need to consider a hypothetical product rather that one already built. This changes our formula slightly - specifically we may not be able to apply a UX flow to drive analysis since we're unsure of the implementation. Instead, we'll look at core behaviors that are indicative of success. Here's the modified formula: Ask clarifying questions State the goal of the feature **Apply a UX flow to drive a"See full answer

Exponent - "This is a Fermi problem — an estimation or approximation problem with limited information and back-of-the-envelope calculations. There's no right answer: interviewers want to understand how you think and how well you can explain your reasoning, rather than what you already know. Recall the formula for Fermi problems: Ask clarifying questions Catalog what you know Make equation(s) Think about edge cases to add to equation **Breakdown components of your equat"See full answer

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer