Machine Learning Interview Questions
Practice 683 real Machine Learning interview questions for 2026 — Machine Learning interview questions drawn from actual interviews with detailed solutions to power your interview preparation. This collection zeroes in on the things interviewers actually evaluate: core ML theory and statistics, applied model evaluation and experimentation, and production ML systems and MLOps. Compared with generic algorithm rounds, ML interviews test both conceptual depth and product- and systems-level judgment. Expect interviews at heavy-weight companies such as Google, Meta, and Amazon to probe tradeoffs (bias‑variance, calibration, evaluation metrics), applied coding and data wrangling, and ML-system design for scalability and reliability. Rounds typically mix short fundamentals questions, a coding or modeling exercise, a systems-design discussion, and behavioral stories about impact. Best prep focuses on hands-on model work, clear explanations of assumptions and failure modes, timed mock interviews that mirror the loop you’ll face, and concise STAR stories that tie ML decisions to business outcomes.

"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."
Address Overfitting with L1 Regularization in Regression
Linear Regression with Many Predictors and Few Observations You fit an ordinary least squares linear regression with 500 predictors and 600 observatio...
Identify and Fix Predictive Model Performance Gaps
Model Review: Month Encoding, Feature Scaling, and Imbalanced Data You are auditing an existing predictive model for operational performance. The curr...
Explain Linear Regression Feature Transformation Equivalence
Explain Linear Regression Feature Transformation Equivalence Linear Regression Feature Representations and High-Dimensional Modelling Context You are ...
Explain Overfitting and Underfitting in Machine Learning
Explain Overfitting and Underfitting in Machine Learning ML Fundamentals and Computer Vision: Core Concepts Instructions You are interviewing for a da...
Detect Data Leakage in Supervised Learning Pipelines
Detect Data Leakage in Supervised Learning Pipelines ML Take‑home: Bias–Variance, Regularization, Leakage, and From‑scratch Logistic Regression Contex...
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...

Implement universal adversarial attack on GPT-2
Robustness Evaluation: Universal Adversarial Prompts for GPT-2 You are in a Machine Learning Engineer interview. Explain how you would build a control...
Explain unsupervised fraud and evaluation
Explain unsupervised fraud and evaluation Unsupervised Fraud Detection: Methods, When to Use Them, and How to Evaluate Without Reliable Labels Context...
How would you design Shop-ad ranking?
Suppose the previous experiment shows that, in some contexts, users are more likely to convert when shown an ad that leads to an in-app Shop rather th...
Answer basic probability and statistics questions
You are given several short, independent probability and statistics questions similar to those in a data / ML screening test. Answer all sub-questions...
Propose an ads recommendation model for shop ads
You need to propose a modeling approach for recommending/ranking shop ads (i.e., which shop ads to show and in what order) for a marketplace app. Desc...
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...
Handle challenges in MMM/MMX
MMM Fragility Diagnosis and Remediation Plan (Weekly, 156 Weeks) Context You inherit a weekly Marketing Mix Model (MMM/MMX) with 156 weeks of data. Th...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Build a package-allocation model for couriers
Automatic Package-to-Courier Assignment with ML + Optimization You previously assigned packages to couriers manually. Design an end-to-end system that...
Design email to avoid Promotions without online tests
Offline Design of a Transactional Email to Minimize Promotions/Spam Classification Context You must finalize the design of a single in‑game transactio...
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...
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 ...
Design a hybrid marketplace fraud system
Design a Fraud Detection System for a Marketplace and Profile Credentials Context You are a data scientist at a two‑sided marketplace where users can ...