Data Scientist Machine Learning Interview Questions
Practice 399 real Machine Learning interview questions for Data Scientist roles. From companies including Meta, Amazon, Google, Capital One, TikTok.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"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."
Handle imbalance, sampling, and overfitting
Practical ML questions (classification and generalization) Answer the following ML engineering/data science questions. A) Class imbalance You’re train...
Normalize features and rank logistic coefficients
You are given a binary classification training dataset: - X: a 2D array of shape (n_samples, n_features) containing numeric features. - feature_names:...
How would you build and evaluate a classifier?
You are building a binary classification model for a business use case such as fraud detection, churn prediction, lead scoring, or content moderation....
Compute gambler’s ruin probabilities and hitting times
A gambler plays a sequence of independent bets. Starting wealth is \(i\) dollars, with absorbing boundaries at \(0\) (ruin) and \(N\) (target). Each r...
How to forecast bike dock demand
You operate a shared city-bike system. For a given dock (station), you want to predict demand in the next hour. Task Design an approach to predict: - ...
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...
Explain K-Fold Cross-Validation and Its Trade-Offs
Technical Phone Screen: Cross-Validation Task You are interviewing for a Data Scientist role. Explain and reason about k-fold cross-validation. Questi...
Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors
Scenario You are tasked with building a dynamic-pricing system for Lyft (a two-sided ride-hailing marketplace) that balances rider demand, rider ETA/c...
Design a Restaurant Recommendation System for Food Apps
Designing a Restaurant Recommendation System for a Food-Ordering App Context You are tasked with designing an end-to-end recommendation system that su...
How would you design an ETA prediction system?
Design an end-to-end ETA (Estimated Time of Arrival) system for a maps / ride-hailing / delivery product. Assume users request an ETA for a trip from ...
Design a hierarchical forecast for transactions
Stripe wants a country×industry daily GMV forecast for the next 90 days (2025-09-01 to 2025-11-29) using 3+ years of history. You have features: day-o...
Perform no-intercept linear regression from two datasets
You are given two pandas datasets to fit an OLS model without an intercept (through origin). Dataset A (features): df_X(user_id, clicks, impressions)....
Decide between two vendors under constraints
You have two third‑party search vendors, A and B, plus historical order‑level data: lead_time_days, unit_price, on_time_rate, defect_rate, min_order_q...
Detail NLP preprocessing and n‑gram choices
Describe your text preprocessing pipeline given the source modality: typed text, scanned/handwritten OCR, or speech-to-text. Specify language handling...
Design and sample for credit default prediction
A bank wants a model to predict 90-day credit card default at account-month level for proactive outreach. Class prevalence in production is about 2% d...
Design an ad-selection system across objectives
End-to-End Ad-Selection System Design Context You must choose, at impression time, which advertiser type to show to a user. There are three advertiser...
Design a hierarchical MF delinquency forecasting system
Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design Context You need to forecast 90+ day delinquency...
Evaluate and monitor a credit risk model
Credit-Risk PD Model: Evaluation Priorities and End-to-End Plan Context: You are deploying a consumer credit probability-of-default (PD) model for 12-...
Implement R² and Compare PCA With/Without Scaling
NumPy-only implementation: R² and PCA (Data Scientist take-home) Implement from scratch using only NumPy (no scikit-learn). Use float64 throughout and...
Forecast response-rate trends with backtesting
Forecasting Response Rate by Job Category and Week Context You are given weekly marketplace data with invitations and responses by job_category and re...