Capital One Data Scientist Machine Learning Interview Questions
Practice 19 real Machine Learning interview questions for Data Scientist roles at Capital One.
"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."
Design a robust fraud detection system
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Design robber detection from surveillance video
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How would you design delay and watchlist models?
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Model flight delays with EDA and explanation
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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-...
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_...
Explain MSE vs MAE, AUC, and imbalance handling
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Choose and justify ML algorithms for tabular prediction
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Present and defend your data challenge end-to-end
10–12 Minute Interviewer-Driven Walkthrough: Recent Data Challenge Provide a concise, structured walkthrough of a real project you led end-to-end. Ass...
Design a production face recognition system
Design an On-Device Face Recognition System for Mobile Access Control Context You are designing a face-based access control system for mobile devices ...
Build and validate a binary classifier
ML Pipeline with Grouped CV, Imbalance Handling, Calibration, and Thresholding Context: You have a labeled dataset where the target is is_active_30d (...
Identify Risks and Improve Imputation Class Implementations
Identify Risks and Improve Imputation Class Implementations Scenario You are reviewing three custom Python imputation classes intended for use in a sc...
Evaluate Python Class Design in Data Pipeline
Evaluate Python Class Design in Data Pipeline Scenario You are reviewing a Python class used in an ML/data pipeline that follows the scikit-learn-styl...
Evaluate OutlierHandler Class for Code Quality and Testing
Code Review: OutlierHandler and Imputer Classes You are given a Python module that implements one OutlierHandler class and three Imputer classes for p...
Diagnose Multicollinearity in Flight Delay Prediction Model
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Design ML deployment with GitHub and Jenkins
Design an end‑to‑end ML deployment for a prediction model using GitHub and Jenkins: 1) Propose a repo layout (src/, features/, data_contracts/, tests/...
Evaluate Models for Credit-Risk Scoring at Capital One
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