Upstart Data Scientist Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Design a Real-Time Personalized Ad Selection System
End-to-End ML System Design: Real-Time Ad Selection Context You need to design a real-time, data-driven ad selection system that personalizes ads for ...
Explain L1 vs L2 and ridge vs lasso
Explain the differences between: 1. L1 vs L2 regularization (how they change the objective, geometry/intuitions, and typical effects on learned parame...
Leverage Existing Model for Low Credit Score Applicants
Expanding a Credit-Risk Model to a New Score Band Scenario Your current probability-of-default (PD) lending model was trained only on applicants with ...
Design Push-Notification System for Airport Surge Pricing
Designing Airport Surge Push Notifications for Drivers Context You are building a real-time system for a ride-hailing platform. When an airport experi...
Implement PAVA spend-smoothing under no-borrowing constraint
Monotone Spending Plan via Isotonic L2 Regression (No-Borrowing) Context: You observe yearly discretionary income profit[1..65] (nonnegative reals) an...
Derive logistic regression objective and gradients
Context: Binary Logistic Regression You are given a binary classification dataset {(x_i, y_i)}_{i=1}^m with labels y_i ∈ {0, 1}. The model uses the si...
Design a Regression Model for Robust Extrapolation Performance
Scenario Onsite machine-learning exercise: your task is to build a regression model using only numerical features that not only fits training data but...
Address Missing Income Bracket in California Housing Data
ML Case: Missing Lowest-Income Bracket in California Housing Data Context You're building a supervised model (regression) to predict California housin...
How to Architect a Personalized Ads Serving System
Full-Funnel Ads Serving System Design Scenario You are asked to architect a full-funnel advertising platform that serves personalized ads to users on ...