System Design Interview Questions (DS & ML)

This document provides a curated list of system design questions tailored for Data Science and Machine Learning interviews. The questions focus on designing scalable, robust, and maintainable systems—from end-to-end ML pipelines and data ingestion frameworks to model serving, monitoring, and MLOps architectures. Use the practice links provided to dive deeper into each topic.

Sno	Question Title	Practice Links	Companies Asking	Difficulty	Topics
1	Design an End-to-End Machine Learning Pipeline	Towards Data Science	Google, Amazon, Facebook	Medium	ML Pipeline, MLOps
2	Design a Scalable Data Ingestion & Processing System for ML	Medium	Amazon, Google, Microsoft	Hard	Data Engineering, Scalability
3	Design a Recommendation System	Towards Data Science	Google, Amazon, Facebook	Medium	Recommender Systems, Personalization
4	Design a Fraud Detection System	Medium	Amazon, Facebook, PayPal	Hard	Real-Time Analytics, Anomaly Detection
5	Design a Feature Store for Machine Learning	Towards Data Science	Google, Amazon, Microsoft	Medium	Data Preprocessing, Feature Engineering
6	Design an Online ML Model Serving Architecture	Towards Data Science	Google, Amazon, Facebook	Hard	Model Deployment, Real-Time Serving
7	Design a Continuous Model Retraining and Monitoring System	Medium	Google, Microsoft, Amazon	Hard	MLOps, Automation
8	Design an A/B Testing Framework for ML Models	Towards Data Science	Google, Facebook, Amazon	Medium	Experimentation, Evaluation
9	Design a Distributed ML Training System	Towards Data Science	Google, Amazon, Microsoft	Hard	Distributed Systems, Deep Learning
10	Design a Real-Time Prediction Serving System	Towards Data Science	Amazon, Google, Facebook	Hard	Model Serving, Real-Time Processing
11	Design a System for Anomaly Detection in Streaming Data	Medium	Amazon, Google, Facebook	Hard	Streaming Data, Anomaly Detection
12	Design a Real-Time Personalization System for E-Commerce	Medium	Amazon, Facebook, Uber	Medium	Personalization, Real-Time Analytics
13	Design a Data Versioning and Model Versioning System	Towards Data Science	Google, Amazon, Microsoft	Medium	MLOps, Version Control
14	Design a System to Ensure Fairness and Transparency in ML Predictions	Medium	Google, Facebook, Amazon	Hard	Ethics, Model Interpretability
15	Design a Data Governance and Compliance System for ML	Towards Data Science	Microsoft, Google, Amazon	Hard	Data Governance, Compliance
16	Design an MLOps Pipeline for End-to-End Automation	Towards Data Science	Google, Amazon, Facebook	Hard	MLOps, Automation
17	Design a System for Real-Time Prediction Serving with Low Latency	Medium	Google, Amazon, Microsoft	Hard	Model Serving, Scalability
18	Design a Scalable Data Warehouse for ML-Driven Analytics	Towards Data Science	Google, Amazon, Facebook	Medium	Data Warehousing, Analytics
19	Design a System for Hyperparameter Tuning at Scale	Medium	Google, Amazon, Microsoft	Hard	Optimization, Automation
20	Design an Event-Driven Architecture for ML Pipelines	Towards Data Science	Amazon, Google, Facebook	Medium	Event-Driven, Real-Time Processing
21	Design a System for Multimodal Data Processing in Machine Learning	Towards Data Science	Google, Amazon, Facebook	Hard	Data Integration, Deep Learning
22	Design a System to Handle High-Volume Streaming Data for ML	Towards Data Science	Amazon, Google, Microsoft	Hard	Streaming, Scalability
23	Design a Secure and Scalable ML Infrastructure	Towards Data Science	Google, Amazon, Facebook	Hard	Security, Scalability
24	Design a Scalable Feature Engineering Pipeline	Towards Data Science	Google, Amazon, Microsoft	Medium	Feature Engineering, Scalability
25	Design a System for Experimentation and A/B Testing in Data Science	Towards Data Science	Google, Amazon, Facebook	Medium	Experimentation, Analytics
26	Design an Architecture for a Data Lake Tailored for ML Applications	Towards Data Science	Amazon, Google, Microsoft	Medium	Data Lakes, Data Engineering
27	Design a Fault-Tolerant Machine Learning System	Medium	Google, Amazon, Facebook	Hard	Reliability, Distributed Systems
28	Design a System for Scalable Deep Learning Inference	Towards Data Science	Google, Amazon, Microsoft	Hard	Deep Learning, Inference
29	Design a Collaborative Platform for Data Science Projects	Towards Data Science	Google, Amazon, Facebook	Medium	Collaboration, Platform Design
30	Design a System for Model Monitoring and Logging	Towards Data Science	Google, Amazon, Microsoft	Medium	MLOps, Monitoring

Questions asked in Google interview

Design an End-to-End Machine Learning Pipeline
Design a Real-Time Prediction Serving System
Design a Continuous Model Retraining and Monitoring System
Design a System for Hyperparameter Tuning at Scale
Design a Secure and Scalable ML Infrastructure

Questions asked in Amazon interview

Design a Scalable Data Ingestion & Processing System for ML
Design a Recommendation System
Design a Fraud Detection System
Design an MLOps Pipeline for End-to-End Automation
Design a System to Handle High-Volume Streaming Data for ML

Questions asked in Facebook interview

Design an End-to-End Machine Learning Pipeline
Design an Online ML Model Serving Architecture
Design a Real-Time Personalization System for E-Commerce
Design a System for Model Monitoring and Logging
Design a System for Multimodal Data Processing in ML

Questions asked in Microsoft interview

Design a Data Versioning and Model Versioning System
Design a Scalable Data Warehouse for ML-Driven Analytics
Design a Distributed ML Training System
Design a System for Real-Time Prediction Serving with Low Latency
Design a System for Secure and Scalable ML Infrastructure