System Design: Scalable Interview Question Bank Platform
Context
Design a multi-tenant SaaS platform enabling organizations to create and manage an interview question bank for live technical interviews. The system must support comprehensive authoring, collaborative review, flexible tagging, difficulty calibration, fast search and browsing, strict versioning, and a menu-style selection flow for interviewers to use in real time.
Target Scale
- Organizations: 5,000
- Total Questions: 2 million
- Total Versions: 50 million
- Total Events (views/uses): 500 million
- Monthly Active Users (authors/interviewers): 200,000
- Peak Read RPS: 20,000
- Peak Write RPS: 1,000
P95 API Latency Targets
- Reads: ≤150 ms
- Writes: ≤300 ms
- Search: ≤300 ms
- Selection Flow: ≤150 ms
Requirements
Core Features
- Authoring: Rich text editing with attachments; collaborative editing and commenting capabilities.
- Management: Tagging (by topics, skills, languages), difficulty calibration, and strict versioning (supporting immutable versions and a draft → review → publish workflow).
- Discovery: Search and browse functionality with filters/facets; support for both semantic and keyword search; intelligent suggestions.
- Interview Flow: A menu-style selection process for live interviews, offering guided, filterable, and diverse question menus.
Technical Specifications to Provide
Candidates are expected to detail the following:
- High-level architecture and data/storage choices.
- API endpoints covering CRUD operations, versioning/publish, tagging, search, selection, calibration, and analytics.
- Data models and indexing strategy (including keyword and semantic indexing, facets, and incremental updates).
- Recommendation/ranking approach for the menu flow (focusing on scoring and diversification).
- Multi-region deployment and data replication strategy.
- Caching layers and invalidation mechanisms.
- Consistency vs. availability trade-offs by subsystem.
- Rate limits and quotas.
- Monitoring, alerting, and Service Level Objectives (SLOs).
- Cost considerations and optimization strategies.
- Permissions/roles and abuse prevention (e.g., moderation, audit logging, anti-scraping measures, usage caps).
- Offline editing capabilities with synchronization and conflict resolution.
Candidates should state any assumptions made and justify their design choices.
AI interview prep
Core dilemma
Balancing the complexity of a highly dynamic, collaborative, and versioned content platform with extreme performance, availability, and sophisticated search/recommendation requirements, all within a multi-tenant, globally distributed SaaS model.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
System Design: Scalable Interview Question Bank Platform
Context
Design a multi-tenant SaaS platform enabling organizations to create and manage an interview question bank for live technical interviews. The system must support comprehensive authoring, collaborative review, flexible tagging, difficulty calibration, fast search and browsing, strict versioning, and a menu-style selection flow for interviewers to use in real time.
Target Scale
- Organizations: 5,000
- Total Questions: 2 million
- Total Versions: 50 million
- Total Events (views/uses): 500 million
- Monthly Active Users (authors/interviewers): 200,000
- Peak Read RPS: 20,000
- Peak Write RPS: 1,000
P95 API Latency Targets
- Reads: ≤150 ms
- Writes: ≤300 ms
- Search: ≤300 ms
- Selection Flow: ≤150 ms
Requirements
Core Features
- Authoring: Rich text editing with attachments; collaborative editing and commenting capabilities.
- Management: Tagging (by topics, skills, languages), difficulty calibration, and strict versioning (supporting immutable versions and a draft → review → publish workflow).
- Discovery: Search and browse functionality with filters/facets; support for both semantic and keyword search; intelligent suggestions.
- Interview Flow: A menu-style selection process for live interviews, offering guided, filterable, and diverse question menus.
Technical Specifications to Provide
Candidates are expected to detail the following:
- High-level architecture and data/storage choices.
- API endpoints covering CRUD operations, versioning/publish, tagging, search, selection, calibration, and analytics.
- Data models and indexing strategy (including keyword and semantic indexing, facets, and incremental updates).
- Recommendation/ranking approach for the menu flow (focusing on scoring and diversification).
- Multi-region deployment and data replication strategy.
- Caching layers and invalidation mechanisms.
- Consistency vs. availability trade-offs by subsystem.
- Rate limits and quotas.
- Monitoring, alerting, and Service Level Objectives (SLOs).
- Cost considerations and optimization strategies.
- Permissions/roles and abuse prevention (e.g., moderation, audit logging, anti-scraping measures, usage caps).
- Offline editing capabilities with synchronization and conflict resolution.
Candidates should state any assumptions made and justify their design choices.