Interview Questions

Anish G. - "How would you improve a water bottle? To start, I want to ask a few clarifying questions about this problem: Are there specific users you had in mind? Start with users: Lifestyle: People that think water bottles are fashionable and buy based off of social media trends, put stickers or decorate their water bottles, hydroflask users, stanley cup, owala Athelete: gatorade squeeze bottle easy quick hydration Traveler, hikers, backpackers: larger capacity nalgenes or durable plastic water bot"See full answer

Donald Y. - "from typing import List def longestcommonprefix(a: List[int], b: List[int]) -> List[int]: prefix = [] for x, y in zip(a, b): if x == y: prefix.append(x) else: break return prefix `"See full answer

Sh R. - "i responded using a multi sourced BFS and in place marking, then i checked the final grid to see if any free spots were left unmarked."See full answer

Krishan K. - "If I could only talk to one customer of my product, I will pick that customer who was earlier a loyal user of my product but now engaging less or have left the product recently. As a PM, two most important metrics that show that your product is worthy for your users are retention & monetisation. If I am seeing a Loyal user not engaging any more with my platform, it shows that either that user has found 10X better substitute to satisfy the same need or that user is not liking my platform any mor"See full answer

Sumit P. - "CQs: Content moderation system finds Inappropriate contents - profanity, violence, privacy concerning. Misinformation - false info, false claim, fomenting wrong views PII Misinformation → Wrong info Twisted info Incomplete info Goal - reliability and trust on the platform Long term increased engagement on informational content RAG system - what and why? RAG system has 3 components Brain - reasoning models Tool"See full answer

Christopher W. - "Stripe Connect is the backbone for marketplaces and platforms like think Shopify, DoorDash, or Lyft, that need to send money to multiple sellers or service providers. Its core promise is simple but vital: enable platforms to pay their users quickly, reliably, and compliantly. If we step back, a good North Star Metric should reflect the real value the product delivers to its users, and it should scale as that value grows. For Connect, that value is the flow of money from platforms to their conne"See full answer

Richa M. - "Before starting the answer I would align on goal, call out the structure where I cover what all am i going to talk about in the interview. Why laying out structure is important because the aprox time available to you is 35 min, so Structure shows the intent and the depth at which you are willing to go and think IMO This is how I would start- Sure before jumping in, I’ll first align on what this product is trying to achieve. Once we’re clear on the goal, I’ll define the key user segments, then"See full answer

Ashish D. - "This is a geometric variation of the "Sliding Window" technique. The core challenge is converting a 2D coordinate problem into a 1D linear problem using angles. The standard approach is the Angular Sweep. By converting the Cartesian coordinates (x,y) of every tree into Polar coordinates (specifically the angle θ), we can process the trees based on the direction they lie in relative to the observer. The Strategy Coordinate Translation: Calculate the relative coordinates (Δx,Δy) for"See full answer

Kartikeya N. - "Clarifying questions: What level of retention are we targeting?: 30/60/90 day?: 30 days retention Do we have any user geography in mind?: No, lets talk global How do we define 30 day retained user? : Any user who comes to the app once in 30 days to do something meaningful How much is the current retention and how much do we want to increase it to?: Lets aim to 2X the current retention in an year Lets start with the Goal of the Adobe Scan app. Its goal is to allow users to capture im"See full answer

Alex N. - "Since question asks about pipeline. I assume the question is about metrics across many dimensions not just prediction Model performance. For the ML Model: I can use accuracy, precision, recall, F1 if it is classification model. In case it is regression model RMSE is good metric for many problems. Data: ML system needs good quality data. The system has to track missing data rate. Distribution of features, if there is no drift from original feature distributions during the training. Pipeline h"See full answer