Interview Questions

Abhishek s. D. - "I would get an personal understanding of the pain-points my customers, & business teams are facing. In my experience, spending more time, going deeper in the problem discovery helped me get a rough idea of the how important this problem is. If this is not resolved - what could be the consequences for our users & the business. Given this broad idea, I just simply evaluate them on value vs effort scale. The higher the score, higher the priority. Effort could be in T-shirt size or sto"See full answer

Disha K. - "Pinterest. It's easy to use, visually aesthetic and has an amazing algorithm to keep you hooked."See full answer

Anjan - "Clarifying Questions: Platforms - Is it a web-based, mobile-based, or both? = Picking both For payment are we partnering with other providers like paypal, stripe? = assuming yes Partnering with shipping companies for shipment ? Is it online delivery? or just pick up within local markets? Expected number of daily users? Users: Sellers: People who sell fruits or vegetables or both Buyers: People who buy fruits and vegetables Note: For product design, I would just focu"See full answer

Tushar A. - "class Node { int val; Node left, right; Node(int v) { val = v; left = right = null; } } class BinaryTree { Node root1, root2; boolean identicalTrees(Node a, Node b) { if (a == null && b == null) return true; if (a != null && b != null) return (a.val == b.val && identicalTrees(a.left, b.left) && identicalTrees(a.right, b.right)); "See full answer

Exponent - "This is another 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"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

JD - "Questions: What is the purpose of the scooters? (commuting in between office buildings or zipping across on the floor or for employees to be able to get to locations nearby e.g. lunch, coffee ) Is the entire workforce in the same building or multiple buildings? If the employees in multiple buildings how far apart are they? Where are the offices located? (lousy weather would mean more employees using scooters to save time and a more convenient transport) Estimation: Planning for highes"See full answer

P D. - " Summary of chapter for students to enable quick learning for revision Summary of books for readers so that they can make better decision to choose the book Summary of vedas to easily understand and teach the religious texts Summary of news articles for readers to quickly review the articles and then deep dive into specific topic Summarizing the legal documents for users to understand easily and ensure the authenticity and correctness of the documents Summarize research reports and s"See full answer

Exponent - "This is another 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"See full answer

Lucas G. - "I'd recommend to adjust p-values because of the increased chance of type I errors when conducting a large number of hypothesis. My recommended adjustment approach would be the Benjamini-Hochberg (BH) over the Bonferroni because BH strikes a balance between controlling for false positive and maintaining statistical power whereas Bonferroni is overly conservative while still controlling for false positives, it leads to a higher chance of missing true effects (high type II error)."See full answer