Practical Resources for System Design and ML Interview Questions (repo+website)
Category: ml_system_design
Date: 2026-03-29
Practical Resources for System Design and ML Interview Questions
Problem Description:
Design a system to host a repository of system design and machine learning interview questions, along with a website to provide access to these resources.
1. Requirements (Functional + Non-functional)
- Functional Requirements:
- Store a large number of system design and machine learning interview questions in a repository.
- Provide a website for users to browse and search through the questions.
- Allow users to filter questions by topic, difficulty level, and tags.
- Enable users to add their own questions to the repository.
- Offer a rating system for users to rate and review questions.
- Non-functional Requirements:
- High availability and scalability to handle a large number of users.
- Fast query response times for searching and browsing through questions.
- Data consistency and integrity across the system.
- Security measures to prevent unauthorized access and data tampering.
2. High-Level Architecture
- Components:
- Frontend: A web application built using a modern frontend framework (e.g., React) to provide a user-friendly interface for browsing and searching through questions.
- Backend: A RESTful API built using a language like Python or Node.js to handle requests from the frontend and interact with the database.
- Database: A relational database management system (RDBMS) like MySQL or PostgreSQL to store the interview questions and user data.
- Storage: An object storage service like Amazon S3 to store large files, such as images and videos.
- Communication:
- The frontend sends HTTP requests to the backend API, which then interacts with the database and storage services as needed.
3. Database Design
- Tables:
- questions: Stores information about each interview question, including the question text, tags, difficulty level, and ratings.
- users: Stores information about each user, including their username, email, and password.
- ratings: Stores ratings and reviews submitted by users for each question.
- Relationships:
- A question can have multiple tags, so a many-to-many relationship exists between questions and tags.
- A user can submit multiple ratings, so a many-to-many relationship exists between users and ratings.
4. Scaling Strategy
- Horizontal Scaling: Use a cloud provider like AWS or Google Cloud to deploy multiple instances of the backend API and frontend application.
- Load Balancing: Use a load balancer to distribute incoming traffic across multiple instances of the backend API.
- Caching: Use a caching layer like Redis or Memcached to store frequently accessed data and reduce the load on the database.
5. Bottlenecks
- Database Queries: Slow database queries can become a bottleneck as the number of questions and user ratings increase.
- Storage Limits: Running out of storage space can become a bottleneck if the system is unable to scale storage horizontally.
6. Trade-offs
- Data Consistency: Trading off data consistency for high availability and scalability can lead to data inconsistencies and errors.
- Security: Trading off security for ease of use and scalability can lead to security breaches and data tampering.
First Principle of System Design:
The first principle of system design is to “Keep It Simple, Stupid” (KISS). This means designing a system that is easy to understand, maintain, and scale.
In the Practical Resources for System Design and ML Interview Questions solution, we followed the KISS principle by:
- Using a simple and intuitive database schema
- Avoiding over-engineering the system with unnecessary features
- Focusing on a modular and scalable architecture
- Using caching and load balancing to reduce the load on the system
By following the KISS principle, we were able to design a system that meets the requirements and is easy to understand and maintain.
Learning Links: