Design a scalable interview question bank

Last updated: Mar 29, 2026

Quick Overview

Design a scalable interview question bank evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Design a scalable interview question bank

Palantir

Jul 26, 2025, 12:00 AM

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Design a scalable interview question bank

System Design: Scalable Interview Question Bank Platform

Context

Design a multi-tenant SaaS platform for organizations to create and manage an interview question bank used during live technical interviews. The system must support authoring, collaborative review, tagging, difficulty calibration, fast search/browse, strict versioning, and a menu-style selection flow used by interviewers in real time.

Assume a target scale such as:

5,000 organizations, 2 million total questions, 50 million versions, 500 million events (views/uses), 200k monthly active users (authors/interviewers), 20k peak RPS reads, 1k peak RPS writes.
P95 API latency targets: reads ≤150 ms, writes ≤300 ms, search ≤300 ms, selection flow ≤150 ms.

Requirements

Core features
- Authoring with rich text and attachments; collaborative editing and comments.
- Tagging (topics, skills, languages), difficulty calibration, and versioning (immutable versions, draft → review → publish workflow).
- Search and browse with filters/facets; semantic and keyword search; suggestions.
- Menu-style selection flow for live interviews (guided, filterable, and diverse question menus).
Technical specifications to provide
- High-level architecture and data/storage choices.
- API endpoints covering CRUD, versioning/publish, tagging, search, selection, calibration, analytics.
- Data models and indexing strategy (keyword + semantic; facets; incremental updates).
- Recommendation/ranking approach for the menu flow (scoring and diversification).
- Multi-region deployment and data replication strategy.
- Caching layers and invalidation.
- Consistency vs availability trade-offs by subsystem.
- Rate limits and quotas.
- Monitoring, alerting, and SLOs.
- Cost considerations and optimizations.
- Permissions/roles and abuse prevention (moderation, audit, anti-scraping, usage caps).
- Offline editing and sync/conflict resolution.

State assumptions where needed and justify design choices.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers Guidance

A scoped requirements summary with concrete non-goals and success metrics.
API, data model, architecture, consistency, capacity, and operations.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions Guidance

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

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