Dataford
Top 24
Prep plan
~4h total · Updated weekly · Last refresh Aug 9

Amazon Web Services Data Scientist Interview Questions

The questions to prepare for a Amazon Web Services Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

Start practicing free →
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance Tradeoff
Recently asked
Amazon Web Services
Bias-Variance Tradeoff in Model Choice
Easy

Explain how the bias-variance tradeoff guides algorithm selection and generalization performance.

Cross-ValidationBias-Variance TradeoffRegularization
Recently asked
Amazon Web Services
More Machine Learning questions with a free account
Statistical Project Walkthrough
Medium

Walk through a past project using hypothesis testing and regression to turn data into a decision.

RegressionHypothesis TestingStatistical Significance
Recently asked
Amazon Web Services
More Statistics & Probability questions with a free account
More Behavioral & Leadership questions with a free account
Diagnose Churn by Customer SegmentMedium

Investigate why one customer segment drives most churn and what actions to take.

User SegmentsChurnDiagnosis
Recently asked
Amazon Web Services
Investigate User Engagement Decline
Medium

Investigate a 15% engagement decline by decomposing the metric, isolating root causes, and proposing actions.

RetentionDiagnosisEngagement Metrics
Recently asked
Amazon Web Services

Sign up to see every question

Create a free account to unlock this list and practice real interview questions.

Get my prep plan
Optimizing Large Analytical SQL Queries
Hard

Explain how to diagnose and optimize a slow analytical query on a multi-terabyte event table using SQL-aware tuning strategies.

JoinsData WranglingCTEs
Recently asked
Amazon Web Services
More SQL & Data Manipulation questions with a free account
Trade-Offs Between ML Algorithms
Hard

Tests ability to choose models based on constraints, performance, and operational considerations.

Cross-ValidationBias-Variance TradeoffAccuracy
Recently asked
Amazon Web Services
More Model Evaluation questions with a free account
Research Motivation
Easy

Tests intrinsic drivers and alignment with sustained research effort and learning.

User NeedsValue Proposition
Recently asked
Amazon Web Services