Image 1

Mocksphere for Students

Image 2

Mocksphere for Teacher

Image 5

Mocksphere for Parents/Guardians

Image 3

Mocksphere for Job Seeker

Image 4

Mocksphere for Recruiter

Online Workshop
Online Workshop Every Week

Join our free weekly interactive learning sessions.

Master AI/ML with instant feedback and personalized learning

"Cogito, ergo sum" (I think, therefore I am)

— René Descartes

René Descartes
Accenture
Activision
Adobe
Airbnb
Airtable
Alteryx
Amazon
Amd
Anthropic
Apple
Atlassian
Bank Of America
Binance
Bitbucket
Blizzard
Box
Broadcom
Bytedance
Canonical
Canva
Circle
Cisco
Coinbase
Company
Crowdstrike
Databricks
Datadog
Deepmind
Dell
Discord
Docker
Doordash
Dropbox
Ea
Ebay
Elastic
Electronic Arts
Epic Games
Etsy
Expedia
Facebook
Figma
Flipkart
Github
Gitlab
Goldman Sachs
Google
Grubhub
Hashicorp
Hello Interview
Accenture
Activision
Adobe
Airbnb
Airtable
Alteryx
Amazon
Amd
Anthropic
Apple
Atlassian
Bank Of America
Binance
Bitbucket
Blizzard
Box
Broadcom
Bytedance
Canonical
Canva
Circle
Cisco
Coinbase
Company
Crowdstrike
Databricks
Datadog
Deepmind
Dell
Discord
Docker
Doordash
Dropbox
Ea
Ebay
Elastic
Electronic Arts
Epic Games
Etsy
Expedia
Facebook
Figma
Flipkart
Github
Gitlab
Goldman Sachs
Google
Grubhub
Hashicorp
Hello Interview
Free Problems
View All Free Problems
Chapter 9: Production LLM Serving Stack
This problem set covers the core concepts from Chapter 9: Production LLM Serving Stack. It explores the anatomy of a production serving system, traffic management, canary deployments, observability, reliability, Kubernetes deployment, and the llm-d framework. Complete the questions in order to build from foundational knowledge to advanced deployment reasoning.
27 pts Medium 99 llm-serving api-gateway architecture +7
Chapter 7: Cross-Request Optimization with SGLang
This problem set tests your understanding of SGLang's cross-request optimization techniques: RadixAttention, zero-overhead scheduling, XGrammar structured output, router-based scaling, session affinity, prefill/decode disaggregation, and advanced MoE optimizations. The questions progress from basic conceptual checks to advanced analytical and coding exercises.
24 pts Medium 99 sglang vllm cross-request-optimization +7
Chapter 11: The Evolving Landscape of Distributed AI
This problem set explores the key concepts from O'Reilly's *Distributed AI Systems*, Chapter 11: The Evolving Landscape of Distributed AI. You will reason about inference economics, on-device AI, MoE routing, edge-cloud coordination, fault tolerance, federated learning, and communication compression.
18 pts Medium 105 inference-economics distributed-ai infrastructure +7
Chapter 10: Distributed Benchmarking and Performance Optimization
This problem set contains eight questions that span the key concepts from the O'Reilly chapter on distributed benchmarking and performance optimization. The questions progress from foundational benchmarking methodology to advanced analysis of multi-node training clusters and inference latency. You will interpret metrics, calculate scaling efficiency, apply Amdahl's Law, and design a diagnostic benchmark plan.
24 pts Easy 98 benchmarking-methodology warmup distributed-systems +7
Chapter 8: Running Distributed Training with SLURM
This problem set tests your understanding of running distributed AI training workloads on SLURM clusters. You will work with SLURM architecture, environment variables, job submission, framework launchers, advanced features, checkpointing, and troubleshooting.
27 pts Easy 102 slurm-architecture daemons hpc +7
Chapter 6: Distributed Inference and vLLM
This problem set tests your understanding of distributed inference and vLLM, covering KV cache and PagedAttention, prefill and decode, tensor/data/pipeline parallelism, expert parallelism, and real-world deployment tradeoffs from Chapter 6.
26 pts Easy 95 kv-cache inference autoregressive-generation +7
Premium Problems
View All Premium Problems
Chapter 4 Fundamental transformation and decomposition of matrices (LAMCM)
This problem set covers fundamental matrix transformations and decompositions including similarity transforms, orthogonal matrices, elementary row operations, triangular decomposition, Cholesky factorization, Jordan canonical forms, singular value decomposition, and Givens/Householder transforms. These concepts are essential for understanding matrix analysis and numerical linear algebra.
30 pts Medium 103 similarity-transform matrix-properties linear-algebra +7
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 2 Numeric Python (NPSCDS)
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 99 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

Knowledge Graphs

USA AI Olympiad

Explore competitive programming and AI contest preparation concepts

View Full Graph
Grade 5 Math

Discover elementary mathematics concepts and learning paths

View Full Graph

Featured Docs

View All PDFs
Cover of System Design Interview: An Insider's Guide Volume 2
System Design Interview: An Insider's Guide Volume 2
116 questions 348 pts
Cover of System Design Interview: An Insider's Guide
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
10 questions 38 pts
Cover of The Principles of Deep Learning Theory
The Principles of Deep Learning Theory
107 questions 418 pts

Featured Books

View All Books
Cover of Distributed AI Systems
Distributed AI Systems
104 questions 261 pts
Cover of Acing the System Design Interview
Acing the System Design Interview
153 questions 456 pts
Cover of Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
240 questions 684 pts
Cover of Hands-On Machine Learning with Scikit-Learn and PyTorch
Hands-On Machine Learning with Scikit-Learn and PyTorch
200 questions 554 pts

Featured Videos

View All Videos
Cover of Flow-Matching vs Diffusion Models explained side by side
Flow-Matching vs Diffusion Models explained side by side
10 questions 29 pts
Cover of Attention in transformers, step-by-step | Deep Learning Chapter 6
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

beginner 467 easy 223 fundamental-concepts 112 optimization 100 performance 87 distributed-systems 86 medium 86 machine-learning 84 fundamentals 83 basic-concepts 80 architecture 79 linear-algebra 78 classification 73 data-structures 73 system-design 66 time-complexity 66 scalability 65 calculation 64 neural-networks 62 machine learning 61
Click on any tag to filter problems by that topic