Machine Learning Interview Questions
Practice 639 real Machine Learning interview questions for 2026 — Machine Learning interview questions drawn from Amazon, Meta, Google, TikTok, and Capital One, with real questions from actual interviews and detailed solutions. This collection is built for interview preparation focused on production-ready ML: expect questions that test modeling and mathematics, coding in Python, ML system design, MLOps and deployment, and modern GenAI topics such as transformer fundamentals, embeddings, and retrieval-augmented generation. Companies emphasize reliability, data quality, and end-to-end ownership as much as algorithmic chops. What’s distinctive: interviews now blend theory, coding, and system thinking — you’ll be evaluated on algorithmic intuition, experiment design and metrics, feature and data engineering, model monitoring and drift detection, and cost/reliability tradeoffs for serving models at scale. To prepare, strengthen fundamentals (linear models, trees, probabilistic reasoning), implement end-to-end projects, rehearse ML system-design case studies, and run mock interviews that combine coding, math, and production scenarios.
"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."
Tune fraud threshold under review capacity and costs
Fraud Triage Thresholding with Calibrated Scores Context You have a fraud model that outputs a calibrated score s ∈ [0, 1] per account, where s ≈ P(fa...
Choose linear regression or decision tree appropriately
Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP Context You have 100,000 i.i.d. observations with features x1 (...
Derive logistic regression objective and gradients
Context: Binary Logistic Regression You are given a binary classification dataset {(x_i, y_i)}_{i=1}^m with labels y_i ∈ {0, 1}. The model uses the si...
Evaluate TPR/FPR, sigmoid, and activations
You have a 70-minute assessment with several ML-fundamentals multiple-choice questions. Answer the following (show calculations where applicable). 1) ...
Explain tokenization and Transformer variants
Tokenization and Transformer Architecture Deep Dive You are asked to explain common tokenization approaches and modern Transformer design choices used...
Explain Transformer Layers and FFN Rationale
Question Explain the Transformer architecture in detail, then walk through the math step by step. 1. Describe the components of each encoder and decod...
Compare float types and design ablation
Floating-point types and ablation study design You are training deep neural networks on modern accelerators that support multiple floating-point forma...
Describe algorithm to find function maximum
Consider a real-valued, differentiable function f(x) defined on R (or more generally on R^n). You have access to an oracle that, for any input x, can ...
Explain DPO and construct its training data
You are working on a project to fine-tune a large language model (LLM) using Direct Preference Optimization (DPO). Answer the following: 1. Conceptual...
Design a System to Recommend Local Restaurant Profiles
Recommending Local Restaurant Pages in the News Feed Context Design a non-ads recommendation system within a large social media app to surface local r...
Choose Between Fine-Tuning and RAG for Client Chatbot
Scenario You are building a client-facing chatbot that must answer questions grounded in the client's proprietary documents. You must choose how to im...
Design an ML Model for Interview Recommendation Pipeline
Scenario You are designing and deploying an ML model that mirrors a real-world recommendation pipeline serving a large product catalog with strict lat...
Optimize Churn Prediction: Feature Engineering and Model Selection
Weekly Churn Prediction (10M users): Feature Engineering, Model Choice, Explainability, and Debugging Scenario You own a weekly churn-prediction pipel...
Describe Your Machine Learning Project Experience
Machine Learning Experience: Walk Through a Project Context You are interviewing for a Data Scientist role. In an HR screen, you’re asked to concisely...
Understand Bias-Variance Trade-off and Regularization Techniques
Rapid-Fire ML Fundamentals — Core Concepts Context You are in a rapid-fire onsite session with a CIO focused on machine-learning fundamentals for a Da...
Identify Fake Accounts Using Machine Learning Techniques
Scenario You are a data scientist at Meta. Fake accounts (bots, spam, scams, impersonation, coordinated inauthentic behavior, and compromised legitima...
Leverage Existing Model for Low Credit Score Applicants
Expanding a Credit-Risk Model to a New Score Band Scenario Your current probability-of-default (PD) lending model was trained only on applicants with ...
Design an LLM agent with RAG and tools
You’re asked to describe how you would build an LLM-based agent that can converse with a user (e.g., an interviewer) and answer questions using an int...
Compute EL and RWA from loan data
Task: Compute Portfolio EL and RWA from Loan-Level PD, LGD, EAD Context You are given an anonymized, loan-level dataset with at least the following fi...
Identify top exposures and mitigate
Portfolio Risk Identification and Mitigation Proposal Context You are evaluating a commercial/corporate lending portfolio. Assume you have loan-level ...