Amazon

E-commerce and cloud computing giant with AWS, the world's leading cloud platform.

5 Rounds ~28 Days Very Hard

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The Interview Loop

Recruiter Screen (30 min)

Standard fit check, behavioral questions, and resume overview.

Technical Loop (3-4 Rounds)

Deep dive into domain knowledge, coding, and system design.

Interview Question Bank

All Roles AI Engineer 47 Cloud Engineer 69 Data Analyst 43 Data Engineer 76 Data Scientist 65 Machine Learning Engineer 15 ML Engineer 52 Product Manager 15 Software Engineer 15

All Topics Statistics 8 SQL 6 Deep Learning 5 Experimentation 5 Leadership 4 Machine Learning 4 ML Systems 3 NLP 3

Data Scientist • Behavioral • medium

Tell me about a data science project where the results surprised you. What did you do?

#Analytical Thinking

Practice

Data Scientist • Behavioral • medium

Describe how you communicated a complex model result to a non-technical stakeholder.

#Storytelling

Practice

Data Scientist • Behavioral • hard

Tell me about a time you had to push back on a business request for an analysis that would be misleading.

#Ethics #Communication

Practice

Data Scientist • Behavioral • medium

Describe a project where you had to iterate significantly on your initial approach.

#Iteration #Learning

Practice

Data Scientist • Behavioral • medium

How do you prioritize between multiple data science requests from different teams?

#Stakeholder Management

Practice

Data Scientist • Behavioral • hard

Tell me about a time your model failed in production. What did you learn?

#Production #MLOps

Practice

Data Scientist • Behavioral • medium

How do you approach ethical considerations in ML model building?

#Fairness #Bias

Practice

Data Scientist • Behavioral • hard

Describe a time you used data to challenge a widely held assumption in your organization.

#Influence #Analytics

Practice

Data Scientist • Behavioral • medium

Tell me about a time you had to deliver a machine learning model under a very tight deadline. What trade-offs did you make?

#Deliver Results #Bias for Action #Trade-offs

Practice

Data Scientist • Behavioral • medium

Tell me about a time you used data to uncover a customer pain point that wasn't immediately obvious. How did you address it?

#Customer Obsession #Dive Deep #Data Storytelling

Practice

Data Scientist • Behavioral • medium

Describe a situation where you found a critical flaw in your own model or analysis after it was deployed. What did you do?

#Dive Deep #Ownership #Insist on Highest Standards

Practice

Data Scientist • Behavioral • medium

Tell me about a time you strongly disagreed with a Product Manager about the direction of a machine learning project. How did you resolve it?

#Have Backbone #Disagree and Commit #Stakeholder Management

Practice

Data Scientist • Coding • hard

Write a SQL query to calculate 30-day user retention.

#Retention #Analytics

Practice

Data Scientist • Coding • hard

How would you write a funnel analysis query in SQL?

#Funnel #Analytics

Practice

Data Scientist • Coding • medium

Write a query to identify duplicate records and deduplicate them.

#Deduplication #Data Quality

Practice

Data Scientist • Coding • medium

Write a SQL query to find the top 3 best-selling products in each category for the last 30 days, considering only orders that were successfully delivered.

#Window Functions #Filtering #Joins

Practice

Data Scientist • Coding • medium

Given a list of customer reviews (strings) and a list of banned keywords, write a Python function to return the top K most frequent valid words across all reviews.

#Hash Maps #Heaps #String Manipulation #NLP

Practice

Data Scientist • Coding • hard

Write a SQL query to calculate the month-over-month retention rate of Amazon Prime members.

#Self Joins #Date Functions #Cohort Analysis

Practice

Data Scientist • Coding • medium

Given a Pandas DataFrame containing user clickstream data (user_id, timestamp, page_url), write code to calculate the average session length. A new session starts if a user is inactive for more than 30 minutes.

#Pandas #Time Series #Sessionization

Practice

Data Scientist • System Design • hard

How would you build a recommendation system? Compare collaborative vs content-based filtering.

#Collaborative Filtering #Content-Based

Practice

Data Scientist • System Design • hard

Design a real-time fraud detection system for a payments platform.

#Fraud Detection #Real-Time ML

Practice

Data Scientist • System Design • hard

How would you build and deploy a churn prediction model?

#Churn #MLOps

Practice

Data Scientist • System Design • hard

Design a feature store. What are its key components?

#Feature Store #MLOps

Practice

Data Scientist • System Design • hard

How would you design a recommendation system for Amazon Prime Video to suggest movies to users who have just finished watching a series?

#Collaborative Filtering #Cold Start #Deep Learning #Real-time Inference

Practice

Data Scientist • System Design • hard

Design a machine learning system to rank search results for Amazon.com. How do you balance relevance, profitability, and shipping speed?

#Learning to Rank #Multi-objective Optimization #Personalization

Practice

Data Scientist • Technical • medium

Explain the bias-variance tradeoff. How does it influence model selection?

#Bias-Variance #Model Selection

Practice

Data Scientist • Technical • medium

What is a p-value? Why is a p-value of 0.05 not always sufficient?

#Hypothesis Testing #p-value

Practice

Data Scientist • Technical • medium

Explain the central limit theorem and its importance in data science.

#CLT #Sampling

Practice

Data Scientist • Technical • easy

What is the difference between Type I and Type II errors?

#Hypothesis Testing #Errors

Practice

Data Scientist • Technical • hard

How do you design an A/B test for a new product feature?

#A/B Testing #Statistics

Practice

Data Scientist • Technical • hard

What is the multiple testing problem? How do you correct for it?

#Bonferroni #FDR

Practice

Data Scientist • Technical • hard

Explain Bayesian vs Frequentist statistics. When would you use each?

#Bayesian #Frequentist

Practice

Data Scientist • Technical • medium

What is a confidence interval? How does it differ from a prediction interval?

#Confidence Interval #Intervals

Practice

Data Scientist • Technical • hard

Explain the curse of dimensionality and its implications for ML models.

#Dimensionality #Feature Engineering

Practice

Data Scientist • Technical • medium

How would you detect and handle multicollinearity in a regression model?

#Multicollinearity #Regression

Practice

Data Scientist • Technical • hard

Explain gradient boosting. How does XGBoost differ from a standard gradient boosting machine?

#Gradient Boosting #XGBoost

Practice

Data Scientist • Technical • medium

How does a Random Forest work? What are its hyperparameters and how do you tune them?

#Random Forest #Hyperparameter Tuning

Practice

Data Scientist • Technical • medium

What is regularization? Explain L1 vs L2 regularization and their effects.

#Regularization #L1 #L2

Practice

Data Scientist • Technical • medium

How do you handle class imbalance in a classification problem?

#Imbalanced Data #SMOTE

Practice

Data Scientist • Technical • medium

Explain the ROC curve and AUC metric. When would you prefer AUC over accuracy?

#ROC #AUC #Metrics

Practice

Data Scientist • Technical • medium

What is cross-validation? Explain k-fold and stratified k-fold.

#Cross Validation #k-Fold

Practice

Data Scientist • Technical • medium

How do you approach feature selection?

#Feature Selection #LASSO

Practice

Data Scientist • Technical • medium

Explain the difference between bagging and boosting.

#Bagging #Boosting

Practice

Data Scientist • Technical • medium

What is principal component analysis (PCA)? What are its limitations?

#PCA #SVD

Practice

Data Scientist • Technical • medium

Explain how backpropagation works.

#Backpropagation #Neural Networks

Practice

Data Scientist • Technical • hard

What is the vanishing gradient problem? How do LSTM and ResNet address it?

#LSTM #ResNet #Gradients

Practice

Data Scientist • Technical • hard

Explain the transformer architecture. What are attention mechanisms?

#Transformers #Attention #BERT

Practice

Data Scientist • Technical • medium

What is transfer learning? How would you fine-tune a pre-trained model?

#Transfer Learning #Fine-Tuning

Practice

Data Scientist • Technical • medium

How would you approach an NLP problem like sentiment analysis from scratch?

#Sentiment Analysis #Text Classification

Practice

Data Scientist • Technical • medium

What is embedding? How do word embeddings like Word2Vec and GloVe work?

#Embeddings #Word2Vec

Practice

Data Scientist • Technical • medium

Explain batch normalization and why it helps training.

#Batch Normalization #Training

Practice

Data Scientist • Technical • medium

How would you detect and mitigate overfitting in a neural network?

#Overfitting #Dropout #Regularization

Practice

Data Scientist • Technical • hard

How would you design an experiment to measure the impact of a new ranking algorithm?

#Experimentation #Metrics

Practice

Data Scientist • Technical • hard

What is a network effect in experimentation? How do you handle SUTVA violation?

#SUTVA #Network Effects

Practice

Data Scientist • Technical • medium

How do you choose a north star metric for a product?

#Metrics #Product Strategy

Practice

Data Scientist • Technical • easy

Explain the difference between a leading indicator and a lagging indicator.

#Metrics #KPIs

Practice

Data Scientist • Technical • hard

How would you identify the root cause of a sudden 20% drop in DAU?

#Root Cause Analysis #Debugging

Practice

Data Scientist • Technical • easy

What is an experiment holdout group?

#Holdout #Control Group

Practice

Data Scientist • Technical • easy

Explain the difference between INNER JOIN, LEFT JOIN, and CROSS JOIN.

#Joins #SQL

Practice

Data Scientist • Technical • hard

How do you monitor model performance in production? What is model drift?

#Model Drift #Monitoring

Practice

Data Scientist • Technical • hard

Amazon is testing a new 'Buy Now' button color. The A/B test shows a statistically significant increase in click-through rate, but overall revenue decreased. How do you investigate this?

#A/B Testing #Cannibalization #Metrics #Causal Inference

Practice

Data Scientist • Technical • medium

Explain the difference between Random Forest and Gradient Boosting. In an Amazon fraud detection use case where data is highly imbalanced, which would you prefer and why?

#Ensemble Methods #Imbalanced Data #Fraud Detection

Practice

Data Scientist • Technical • hard

How would you build a time-series forecasting model to predict the inventory demand for a highly seasonal product during Prime Day?

#Time Series #Forecasting #ARIMA #XGBoost

Practice

Data Scientist • Technical • easy

You are evaluating a binary classifier for detecting defective items in an Amazon fulfillment center. The defect rate is 0.1%. Why is accuracy a poor metric here, and what metrics would you use instead?

#Evaluation Metrics #Precision #Recall #PR-AUC

Practice

Data Scientist • Technical • hard

We want to test a new pricing algorithm for Amazon third-party sellers. If we randomize at the seller level, how might network effects bias the A/B test results, and how would you design the experiment to mitigate this?

#A/B Testing #Network Effects #Switchback Testing #Cluster Randomization

Practice

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