Amazon Data Scientist Machine Learning Interview Questions
Practice 39 real Machine Learning interview questions for Data Scientist roles at Amazon.
"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."
Predicting the Next Elevator Call Location
This machine learning question tests the ability to design a predictive model for spatio-temporal demand forecasting, covering problem framing, featur...
Evaluate NLP Classification Models
You are interviewing for a Data Scientist internship at Amazon. The interviewer asks you to walk through how you think about an NLP classification pro...
Explain random forests, bagging, and evaluation
This question evaluates understanding of ensemble learning and model evaluation, covering Random Forest aggregation, feature subsampling, bagging vers...
Derive and compare core ML and RL methods
ML Fundamentals Technical Screen — Multi‑part Question Context: You are given a set of core machine learning topics to address rigorously. For each pa...
Choose regularization norms and model formulations
Regularization and model choice. 1) For linear and logistic regression, write the objective functions with L0, L1, L2, and L-infinity penalties in bot...
Write and explain gradient descent pseudocode
Task: Batch Gradient Descent for Linear Regression (with Intercept) You are interviewing for a Data Scientist role and are asked to implement batch gr...
Design and evaluate a RAG system
You are interviewing for an L5 Data Scientist role focused on LLM applications. Design a retrieval-augmented generation (RAG) system for an internal q...
Implement Batch Gradient Descent for Linear Regression
Batch Gradient Descent for Linear Regression You are building a linear regression model from scratch and will optimize the parameters using batch grad...
Design an end-to-end spam detection system
Design an End-to-End Email Spam Detection System You are asked to design a production-grade email spam detection system that meets the following const...
Apply Double ML with text-address features
Estimate the ATE of a First Reminder on CSAT via Double Machine Learning (DML) Context You have observational data on customer satisfaction (CSAT) sur...
Design fraud detection across channels with unknowns
This question evaluates a data scientist's competence in designing and operationalizing multi-channel fraud detection systems, covering cost-sensitive...
Diagnose Bias–Variance Trade-off in Supervised Learning
Diagnose Bias–Variance Trade-off in Supervised Learning Supervised Learning Review (Customer-Facing Ranking Context) You are designing and evaluating ...
Compare RNNs and Transformers for Long-Sequence Text Classification
Compare RNNs and Transformers for Long-Sequence Text Classification Scenario You are designing a long-sequence text classification system under tight ...
Choose Models for Imbalanced Data and Time-Series Forecasting
Choose Models for Imbalanced Data and Time-Series Forecasting Scenario You must choose and tune models for (a) forecasting marketplace demand with sea...
Handle Missing Values and Choose ML Algorithms Wisely
ML Interview: Core Modeling Concepts You are in a technical phone screen for a Data Scientist role. Assume primarily tabular datasets and address both...
Compare Random Forests vs Gradient Boosting rigorously
Technical ML Choice: Random Forest vs. Gradient-Boosted Trees for Large-Scale Binary Classification Problem Setup You need to choose between a Random ...
Explain core ML concepts and metrics
You are interviewing for a Data Scientist role. Answer the following ML fundamentals questions clearly and concisely. Concepts 1. Explain the bias–var...
Optimize Feature Selection and Handling in Machine Learning Models
Optimize Feature Selection and Handling in Machine Learning Models You are building a customer propensity model to predict whether a user will purchas...
Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability
Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability Large-Scale Recommendation System: Ensembles, Overfitting, Metrics, Architectu...
Design end-to-end regression for energy demand
End-to-End Daily Energy Prediction for Commercial Buildings Context You are asked to design and justify an end-to-end regression system that predicts ...