Learn Topics
Master concepts step-by-step through guided MCQs and detailed explanations.
ML System Design Fundamentals
15 QsMaster ML System Design Fundamentals interviewer-level concepts.
Problem Framing
14 QsMaster Problem Framing interviewer-level concepts.
Data Pipeline Design
12 QsMaster Data Pipeline Design interviewer-level concepts.
Feature Engineering At Scale
10 QsMaster Feature Engineering At Scale interviewer-level concepts.
Feature Stores
9 QsMaster Feature Stores interviewer-level concepts.
Training Infrastructure
9 QsMaster Training Infrastructure interviewer-level concepts.
Model Serving Patterns
8 QsMaster Model Serving Patterns interviewer-level concepts.
Online Vs Batch Inference
8 QsMaster Online Vs Batch Inference interviewer-level concepts.
Ab Testing And Experimentation
9 QsMaster Ab Testing And Experimentation interviewer-level concepts.
Monitoring And Observability
8 QsMaster Monitoring And Observability interviewer-level concepts.
Recommendation System Design
8 QsMaster Recommendation System Design interviewer-level concepts.
Search System Design
8 QsMaster Search System Design interviewer-level concepts.
LLM Serving Infrastructure
7 QsMaster LLM Serving Infrastructure interviewer-level concepts.
RAG System Design
7 QsMaster RAG System Design interviewer-level concepts.
GenAI System Design Patterns
7 QsMaster GenAI System Design Patterns interviewer-level concepts.
ML System Trade Offs
7 QsMaster ML System Trade Offs interviewer-level concepts.
Targeted Practice
Mix and match topics by difficulty to solidify your understanding.
Skill Assessments
Validate your expertise with timed, industry-standard tests.
System Design Foundations
Back-of-envelope estimation, ML vs non-ML decisions, NFR trade-offs, and problem framing — the two cornerstones every ML system design interview starts with. Includes classic phrasing traps and scenario-based reasoning.
Data Pipelines & Feature Engineering
Lambda vs Kappa, exactly-once semantics, schema evolution, temporal leakage, high-cardinality encoding, and embedding strategies at scale. Tests pipeline reasoning and the subtle traps that break production feature systems.
Feature Stores & Training Infrastructure
Online/offline store consistency, point-in-time correctness, distributed training strategies (data vs model parallelism), ZeRO optimizer stages, and GPU utilization bottlenecks. Covers the storage-to-training pipeline that separates ML practitioners from ML engineers.
Model Serving & Inference Modes
Blue-green vs canary deployments, shadow mode, multi-armed bandit routing, batch vs real-time inference cost trade-offs, and hybrid architectures. Tests the production deployment reasoning that interviews focus on most.
Experimentation & Observability
A/B test validity, SUTVA violations, novelty effect, SRM detection, feature drift monitoring, data quality alerting, and feedback loop identification. Two of the most misunderstood ML system components — together in one test.
Recommendation & Search Systems
Two-tower models, popularity bias, diversity-relevance trade-offs, BM25 vs semantic search, ANN indexes (HNSW), and cascade ranking. Two of the most common ML system design interview topics — side by side.
LLM Serving & RAG Design
KV cache management, continuous batching, PagedAttention, chunking strategies, retrieval quality vs hallucination trade-offs, and hybrid dense-sparse retrieval. The two hottest ML system design topics in modern interviews.
GenAI Patterns & ML System Trade-offs
Agent orchestration, prompt injection attack surfaces, guard-rail design, Pareto frontier optimization, latency vs accuracy trade-offs, and the irreversibility of production ML decisions. Caps the curriculum with the hardest design thinking in modern ML.
Mock Interview: ML Systems Essentials
Simulates an entry-level ML system design interview round. 10 scenario-based questions covering all 16 topics — each with a classic reasoning trap. Great for first mock attempt or knowledge check before a FAANG phone screen.
Mock Interview: Foundation Check
Second easy mock — different questions, same difficulty ceiling. Tests the complementary half of the fundamentals not covered in Mock 1. Includes topics 03, 05, 06, 08, 12, 14 to complete the easy-level tour of all 16 topics.
Mock Interview: Applied ML Systems
Simulates a mid-level ML engineering interview. 12 scenario-based questions requiring multi-step reasoning across pipeline design, training infrastructure, experimentation, LLM serving, and GenAI patterns. Each question contains a deceptive option that trips up candidates with 2–3 years of experience.
Mock Interview: Engineering Decisions
Second medium mock — a fresh set of 12 questions emphasizing engineering trade-offs: cost vs latency, batch vs real-time, canary deployment criteria, and monitoring for ML-specific failure modes. Complementary to Mock 3 with no repeated questions.
Mock Interview: Senior ML Engineer
Simulates a senior ML engineer interview loop. 15 hard scenario questions spanning queuing theory at scale, distributed training internals, KV cache thrashing, multi-hop RAG failure modes, and production feedback loops. Every question is a FAANG-caliber trap with non-obvious answers.
Mock Interview: Principal Engineer Round
Second hard mock — 15 different questions at the same difficulty ceiling. Emphasizes ZeRO optimizer stages, HNSW graph degradation, agentic loop prevention, gRPC routing pitfalls, and statistical validity in experimentation. No repeated questions from Mock 5.
Elite Assessment: ML Architecture Mastery
18 of the hardest ML system design questions in the bank. Covers all 16 topics at maximum depth. Questions test multi-step architecture reasoning, production failure analysis, and the non-intuitive behavior of distributed ML systems. Intended for senior/staff engineers preparing for AI architect screening or ML platform tech lead rounds.
Elite Assessment: Production ML at Scale
18 elite-level questions with zero overlap from Elite 1. Focuses on production failure modes: causal framing failures, Flink state thrashing, TOCTOU race conditions in async LLM pipelines, Thompson Sampling incompatibility with two-tower models, and PPV/NPV at population scale. Combines questions from the hard practice bank with the hardest questions from the per-topic files — the final boss of the ML system design curriculum.