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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
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Chapter 3 Working with data (DATE)
This problem set covers key concepts from Chapter 3 "Working with data" of D3.js in Action. You'll practice data types, dataset formats, data loading and formatting, data binding, D3 scales, and adding labels to visualizations. These problems progress from basic concepts to practical implementation skills needed for D3.js development.
28 pts Medium 99 data-types quantitative-data qualitative-data +7
Chapter 13 Future Medallion Architectures (BMA)
This problem set explores the integration of Generative AI and Large Language Models (LLMs) with Medallion architectures. The problems cover unstructured data processing, RAG patterns, LLM integration scenarios, and future trends in data management. Each question tests understanding of how traditional Medallion layers (Bronze, Silver, Gold) adapt to handle unstructured data and leverage AI capabilities for enhanced data processing and insights.
27 pts Medium 100 rag-pattern llms unstructured-data +7
Chapter 12 Medallion Governance and Security (BMA)
This problem set covers key concepts from Chapter 12 on Medallion Governance and Security, including data governance frameworks, Unity Catalog implementation, data contracts, and security measures in federated architectures. These questions test understanding of governance principles across Medallion layers, security implementations, and practical applications of data contracts.
30 pts Hard 101 data-governance medallion-architecture layer-objectives +7
Chapter 11 Scaling the Medallion Architecture (BMA)
This problem set covers key concepts from Chapter 11 on scaling Medallion architectures, including data mesh principles, decentralized data management, Medallion mesh concepts, and variations in Medallion inner architecture. These problems test your understanding of how organizations can scale their data management practices using multiple Medallion architectures and tailored approaches to meet diverse business needs.
23 pts Medium 99 data-mesh decentralized-data-management operating-model +7
Chapter 7 Streamline the Gold Layer (BMA)
This problem set covers key concepts from Chapter 7 on designing and implementing the Gold layer in Medallion architectures. You'll be tested on star schema design, SCD2 implementation, data governance, and practical implementation using Microsoft Fabric and Microsoft Purview. These questions progress from foundational concepts to advanced implementation details.
40 pts Hard 98 gold-layer medallion-architecture star-schema +7
Chapter 6 Build the Silver Layer (BMA)
This problem set covers key concepts from Chapter 6 on building the Silver layer in Medallion architectures. You'll be tested on metadata-driven approaches, data cleansing, historization, and various data transformation frameworks. The problems progress from foundational concepts to practical implementation scenarios.
30 pts Medium 95 metadata-driven automation metastore +7
Premium Problems
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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

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Featured Docs

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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
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

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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
Cover of Deep Reinforcement Learning Hands-On - Third Edition
Deep Reinforcement Learning Hands-On - Third Edition
222 questions 720 pts

Featured Videos

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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
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

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beginner 459 easy 217 fundamental-concepts 112 optimization 100 performance 87 distributed-systems 85 medium 85 machine-learning 84 fundamentals 83 basic-concepts 80 linear-algebra 78 architecture 77 classification 73 data-structures 73 system-design 66 time-complexity 66 scalability 65 calculation 63 neural-networks 62 machine learning 61
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