LinkedIn Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
"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."
Explain Logistic Regression, Backprop, and Adam
Walk through the mathematical foundations that connect logistic regression to modern deep-learning training. The interviewer expects you to write the ...
Answer practical ML foundations questions
In an ML interview, you are asked a series of practical ML foundation questions: 1) Model outputs probabilities. When do you need probability calibrat...
Explain variance reduction in random forests
This question evaluates understanding of variance reduction in ensemble methods, the impact of inter-tree correlation on averaged predictors, and the ...
Handle imbalance, sampling, and overfitting
This question evaluates a data scientist's proficiency in machine learning topics including handling class imbalance, selecting and interpreting evalu...
Handle imbalance, validate samples, and avoid overfitting
This question evaluates competencies in handling class imbalance, choosing and interpreting evaluation metrics and decision thresholds, validating sam...
Explain activations, losses, and Adam
This question evaluates understanding of neural network building blocks (layers and activation functions), comparative properties of activation/gating...
Explain overfitting vs underfitting and fixes
This question evaluates understanding of model generalization in supervised machine learning, focusing on the concepts of overfitting and underfitting...
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...
Handle imbalance, sampling, and overfitting
Machine Learning Fundamentals: Imbalance, Sampling, Overfitting, and Regularization You are asked several machine learning fundamentals questions in a...
Train with imbalanced sampled data
You are training a binary classifier on a very large dataset where the positive class is rare. Because the full dataset is too large to train on direc...
Design a short-video recommender system
This question evaluates a data scientist's competency in end-to-end machine learning system design for recommender systems, including retrieval and ra...
Explain Core ML Fundamentals
Review ML fundamentals including logistic regression, cross-entropy loss, batch versus stochastic gradient descent, batch size tradeoffs, overfitting,...