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Free Problems
9 Design a notification/alerting service
This problem set covers key concepts from Chapter 9: Design a notification/alerting service. The problems test understanding of functional requirements, non-functional requirements, system architecture, and design considerations for building a scalable notification service that supports multiple channels like email, SMS, and push notifications.
32 pts Medium 99 functional-requirements uptime-monitoring infrastructure-dependencies +7
8 Design a rate-limiting service
This problem set covers key concepts from the O'Reilly chapter "8 Design a rate-limiting service" including rate limiting algorithms, architectural approaches, tradeoffs, and implementation considerations. The problems progress from basic concepts to advanced distributed system design challenges.
28 pts Medium 103 rate-limiting security traffic-management +7
7 Design Craigslist
This problem set covers key concepts from the O'Reilly chapter "7 Design Craigslist" focusing on system design for a classifieds application. Topics include user stories, API design, SQL schema, high-level architecture, caching strategies, scaling approaches, and tradeoffs between different design decisions. Each question tests understanding of real-world system design challenges and solutions discussed in the chapter.
30 pts Medium 103 user-stories requirements system-design +7
6 Common services for functional partitioning
This problem set covers key concepts from Chapter 6 on functional partitioning, including API gateways, service meshes, metadata services, and API paradigms. These problems test your understanding of how to centralize cross-cutting concerns and make architectural decisions for scalable systems.
27 pts Medium 100 api-gateway cross-cutting-concerns system-architecture +7
5 Distributed transactions
This problem set covers distributed transactions concepts from Chapter 5, including event sourcing, Change Data Capture (CDC), sagas, choreography vs. orchestration, and maintaining data consistency across multiple services. These problems test your understanding of how to handle distributed writes and ensure data consistency in microservices architectures.
22 pts Medium 101 distributed-transactions acid-properties consistency +7
4 Scaling databases
This problem set covers key concepts from Chapter 4 "Scaling databases" including storage services, replication techniques, database sharding, event aggregation, caching strategies, and tradeoffs between different database scaling approaches. These problems test understanding of how to design scalable database systems and make appropriate technology choices based on system requirements.
29 pts Medium 104 storage-classification database-types system-design +7
Premium Problems
Python I/O and Data Pipeline Assessment - Part 4
20 questions focused on PyTorch Dataset/DataLoader design: map/iterable datasets, transforms, custom collate/padding, worker seeding/sharding, num_workers/pin_memory/prefetch_factor, caching, memmap/shared memory, batching by size, profiling, and performance tuning.
10.00 60 pts Medium 98 torch.utils.data.dataset pytorch dataset +7
Chapter 02 - Numeric Python
This problem set covers key concepts from Chapter 2: Vectors, Matrices, and Multidimensional Arrays. The problems test understanding of NumPy array fundamentals, including array creation, indexing, slicing, operations, and vectorized computing. Each question is designed to reinforce the core concepts presented in the chapter.
5.00 26 pts Medium 97 numpy-arrays array-attributes shape +7
USAAIO 2025 R1P3 - Logistic Regression Implementation
This problem focuses on implementing logistic regression from scratch using the Titanic dataset. You will work through data pre-processing, mathematical derivations, and implement both gradient descent and Newton's method for logistic regression. The dataset contains passenger information from the Titanic, and your goal is to predict survival based on various features.
10.00 48 pts Easy 93 data-loading pandas data-exploration +7
USAAIO 2025 R1P2 - Basics of Neural Network - From Linear Regression to DNN Training
This problem is about the basics of neural network. Each part has its particular purpose to intentionally test you something. Do not attempt to find a shortcut to circumvent the rule. And all coding tasks shall run on CPUs, **not GPUs**.
10.00 36 pts Easy 96 learning-rate-scheduler pytorch optimization +12
USAAIO 2025 R1P1 - Fibonacci Matrix Form
Let us consider the following sequence: $$ F_n = F_{n-1} + F_{n-2},\ \forall\ n \ge 2. $$
8.00 27 pts Medium 96 fibonacci sequence linear algebra matrix form +7
IAIO 2024 Part 2 - Machine Learning Algorithms and Deep Learning
This problem covers the remaining categories of the 2024 International Artificial Intelligence Olympiad (IAIO), focusing on machine learning algorithms and deep learning. You'll work through practical implementations of k-means clustering, deep learning architectures, and advanced machine learning theory including kernel methods and the Perceptron algorithm. The problems cover: - K-means clustering algorithm implementation and convergence - Deep learning architectures (DALL-E, Transformers) - Perceptron algorithm and kernel methods - Mathematical proofs and theoretical analysis - Parameter counting and computational complexity
10.00 44 pts Hard 99 k-means clustering euclidean distance machine learning +7

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Featured PDFs

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Cover of System Design Interview: An Insider's Guide Volume 2
System Design Interview: An Insider's Guide Volume 2
116 questions 348 pts
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System Design Interview: An Insider's Guide
108 questions 317 pts
Cover of UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
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The Principles of Deep Learning Theory
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Featured Books

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Cover of Acing the System Design Interview
Acing the System Design Interview
153 questions 456 pts
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Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
190 questions 543 pts
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Hands-On Machine Learning with Scikit-Learn and PyTorch
200 questions 554 pts
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Deep Reinforcement Learning Hands-On - Third Edition
222 questions 720 pts

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Cover of Flow-Matching vs Diffusion Models explained side by side
Flow-Matching vs Diffusion Models explained side by side
10 questions 29 pts
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Attention in transformers, step-by-step | Deep Learning Chapter 6
10 questions 30 pts
Cover of Knowledge Distillation: How LLMs train each other
Knowledge Distillation: How LLMs train each other
10 questions 27 pts
Cover of Diffusion Model
Diffusion Model
10 questions 32 pts

Popular Topics

machine learning 56 deep learning 40 neural networks 35 reinforcement learning 33 system-design 28 grade5 27 optimization 14 large language models 13 attention mechanisms 13 combinatorics 13 system-architecture 13 natural language processing 12 aime problems 12 Number Sense 12 scalability 11 beginner 10 number theory 10 performance 10 transformers 9 capacity-planning 9
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