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."
Predict Future Car Sale Prices
You are given transaction-level data from car dealers for calendar year 2018, and you need to predict vehicle sale prices for transactions that will h...
How would you forecast bike demand?
You are given historical data from a shared city-bike system and asked to predict usage for a specific docking station during the next hour. Assume yo...
How to rank statements by likelihood
You are given a "likelihood" / "data interpretation" test. For each question: - You are shown a data representation (e.g., a large table, a scatter pl...
How predict vehicles’ turn direction at intersection?
At an intersection, there are N vehicles stopped or moving slowly. For each vehicle you have historical time-series data up to the current time: - Pos...
Explain PCA and L2 Normalization in Machine Learning
Scenario Experian DataLabs Data Scientist technical screen — a machine-learning deep-dive on the modelling choices used in your project, mixed with co...
Design a Ride-Hailing ETA System
You are a Data Scientist at a ride-hailing company. Design an ETA system used in the rider and driver apps to estimate both pickup ETA and trip ETA. D...
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...
Explain train-test generalization gap
A model performs very well on the training set but much worse on a held-out test set. Explain why this can happen and how you would diagnose and fix i...
Explain and quantify bias-variance tradeoff
Bias–Variance Tradeoff: Intuition, Derivation, and Practical Tuning Task Explain the bias–variance tradeoff at two levels and connect it to model tuni...
Design an end-to-end spam detection system
Design an End-to-End Email Spam Detection System You are asked to design a production-grade email spam detection system that meets the following const...
Design real-time payments fraud model under constraints
Real-Time ML Policy Design: Prevent Unauthorized Purchases by Minors Context: You need to reduce unauthorized purchases by minors using their parents'...
Handle imbalance, validate samples, and avoid overfitting
Answer the following applied ML questions. 1) Class imbalance You’re building a binary classifier where positives are rare. - What are practical ways ...
Build and evaluate airline delay prediction model
You are given several CSVs for the classic airline delay challenge with columns like flight_date, carrier, flight_num, origin, dest, sched_dep, sched_...
Model flight delays with EDA and explanation
Predicting 15+ Minute Arrival Delays at Scheduled-Departure Time You are building a binary classifier that predicts whether a domestic flight will arr...
Explain fraud types and evaluate a fraud model
You are interviewing for a Fraud Data Scientist role at PayPal. Answer the following: 1) List common fraud types relevant to payments (e.g., account t...
Model Driver Acceptance Probability
Design a machine learning system to predict the probability that a driver accepts a trip or delivery offer. Your answer should cover: - the prediction...
Explain KNN and PCA and key tradeoffs
In a Data Scientist internship interview, you are asked ML fundamentals: 1) K-Nearest Neighbors (KNN) - Explain how KNN works for classification and r...
Explain RF optimization and variable-importance pitfalls
Optimize and Regularize a Random Forest Regressor for Tabular Data Context: You are training a Random Forest (RF) regressor on tabular data and need t...
Optimize precision–recall under class imbalance
You have extreme class imbalance (positive rate ~1%). You score 12 examples as follows (id, true_label, score): A,1,0.92; B,0,0.90; C,0,0.88; D,0,0.70...
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...