Data Scientist Machine Learning Interview Questions
Practice 434 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."
Optimize Predictive Analytics: Feature Engineering to Model Evaluation
End-to-End Predictive Analytics Project Walkthrough You are interviewing for a Data Scientist role. The interviewer asks you to describe a predictive ...
Choose Metrics for Evaluating Fake-User Classifier
Choose Metrics for Evaluating a Fake-User Classifier A sudden spike in daily average comments may be driven by fake users. You are asked to build a bi...
Develop a Restaurant-Recommendation Engine with Logistic Regression
Develop a Restaurant Recommendation Engine with Logistic Regression You are designing a restaurant recommendation engine for a social app. You need to...
Compare Regularization Techniques and Their Use Cases
Compare Regularization Techniques and Their Use Cases This technical phone screen asks about model evaluation, regularization, and regression basics f...
Evaluate Factors Before Replacing Recommendation Model
Evaluate Factors Before Replacing a Recommendation Model A large ads platform has built a new recommendation or ranking model and plans to deprecate t...
Design and Validate Initial Restaurant Recommendation Model
Design and Validate an Initial Restaurant Recommendation Model You are designing a first-iteration machine-learning model to recommend restaurants to ...
Minimize L1 Distance with k Cluster Centers in Array
One-Dimensional k-Center Clustering With L1 Distance You are given an array of n integers on a number line and an integer k, where 1 <= k <= n. Place ...
Employ Collaborative Filtering for Personalized Recommendation Lists
Collaborative Filtering and Ranking for Personalized Recommendation Lists You are releasing a new recommendation feature that must generate personaliz...
Classify Reviewers Using Bayesian Probability for Accuracy Analysis
Classify Reviewers With Bayesian Probability You are auditing reviewers who may be lazy or careful. Each reviewer completes n gold-standard review tas...
Design a Restaurant Recommendation System for Food Apps
Design a Restaurant Recommendation System for a Food-Ordering App You are designing an end-to-end recommendation system that suggests restaurants to u...
Describe algorithm to find function maximum
This question evaluates understanding of gradient-based optimization and numerical methods for locating extrema, assessing competency in interpreting ...
Derive expected inversions and mean distribution
This question evaluates understanding of permutation inversion statistics, computation of random-variable moments (expectation and variance), and the ...
Predict Seller Intent From Subscription Data
You are given a take-home dataset, seller_intent_take_home_dataset.csv, containing about 5,000 new subscription records from a website-building platfo...
Build and iteratively improve sentiment classifier
You need to build a sentiment classification model (e.g., positive/neutral/negative) for user-generated text. You already shipped an initial version, ...
Why Is the Sample Mean Approximately Normal for Large Samples?
In statistics we routinely treat the average of a large sample as if it were normally distributed — for example, when building confidence intervals fo...
How would you manage precision/recall for fraud detection?
This question evaluates a candidate's competency in applied machine learning for fraud detection, covering model performance measurement, thresholding...
Reduce LLM hallucination and handle class imbalance
This question evaluates applied machine learning competencies including LLM safety and hallucination mitigation, retrieval-augmented generation and to...
Derive and regularize logistic regression
This question evaluates proficiency in logistic regression theory and regularization, GLM versus OLS assumptions, class imbalance handling and calibra...
Build and evaluate donation propensity model
You need a model to maximize expected net revenue from solicitations. Costs: online reach costs $1 per person; gala attendance costs $100 per attendee...
Design hashtag recommender with cold start
This question evaluates expertise in recommender-system design, feature engineering, ranking and learning-to-rank models, cold-start strategies, evalu...