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."
Compare CNN/RNN/LSTM and implement K-means
Deep Learning Concepts and K-means Implementation (Onsite ML Interview) This is a two-part onsite round for a Data Scientist role: a conceptual deep-l...
Design and critique an abuse-detection ML system
ML System Design: Abusive Content Detection and Triage (Trust & Safety) Context: You are designing an ML system to identify and triage abusive content...
Design a short-video recommendation system
Design a recommendation system for a short-video feed product. Your answer should cover the full pipeline: 1. Objective and labels: Define what the sy...
Forecast bikes available at a station
Data Analysis / Forecasting Prompt You are given historical Citi Bike (bike-share) trip and station status data. Each station has a fixed dock capacit...
Design a ride-hailing ETA system
During a first-round Data Scientist interview, you are asked a product and machine learning case question: Design an estimated time of arrival (ETA) p...
Improve Model Generalization with Cross-Validation and Feature Engineering
Predict Next-Month Orders: Train/Test Split, Pipeline, and AUC Context You are given a cleaned tabular retail dataset as a pandas DataFrame df. The bi...
Design a Machine Learning Recommendation System Pipeline
System Design: End-to-End ML Recommendation System Scenario You are building an end-to-end machine-learning-powered recommendation system for a large ...
How would you build UberEats ranking?
UberEats wants to improve its recommendation or ranking system for restaurants shown to users on the home feed or in search results. Design the machin...
Test whether samples follow a binomial distribution
You collect a dataset that you believe comes from a binomial process. Each observation is a count of successes. - You have i.i.d. samples \(x_1,\dots,...
Design navigation-safety simulation parameters and experiments
You must design a simulation-based evaluation for navigation safety of an autonomous agent. Be specific: a) Enumerate scene parameters to vary and jus...
Design a robust conversion propensity model
Daily Notification Propensity Model (Top-20% Targeting) Context You need to score users once per day with the probability they will make a purchase wi...
Extract companies from noisy text
Extracting Company Names from Noisy Resumes and Web Snippets Context You receive messy resume text (PDF-to-text/OCR, varying casing) and scraped web s...
Design ETA prediction for Uber rides
System Design: Real‑Time Pickup and Drop‑off ETA Prediction Context: You’re designing an end‑to‑end system that predicts pickup and drop‑off ETAs at t...
Design a restaurant recommender under constraints
Design a Restaurant Recommendation System (Food Delivery App) Context - Goal: Return the top-20 restaurant recommendations within 5 miles in under 100...
Build and evaluate an order prediction model
Predict 7-Day Order Completion from First Session You are building a binary classifier to predict whether a guest will complete an order within 7 days...
Build a late-delivery risk model
Predict Late Delivery Risk at Order Creation Context You are given an anonymized dataset of marketplace orders with timestamps, store/customer/market ...
Explain variance reduction in random forests
Consider a random forest (or bagged ensemble) that predicts at a fixed input \(x\) by averaging \(B\) tree predictions: \[ \hat f_B(x) = \frac{1}{B}\s...
Explain Medical AI Data and Evaluation
You are discussing a prior project on medical conversational AI. Assume proprietary production data is limited, so you may begin with open-source heal...
Explain core ML concepts and metrics
You are interviewing for a Data Scientist role. Answer the following ML fundamentals questions clearly and concisely. Concepts 1. Explain the bias–var...
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...