This one sprawled in every direction and I kept second-guessing which part to anchor on first.
Start by clarifying requirements and scale, then design the data model and APIs with idempotency and concurrency in mind. Walk through the ticket lifecycle, RBAC, notifications, and reporting, and finish with scalability and trade-offs.
Pro tip: Emphasize idempotency and concurrency control early, as they are critical for a system serving both humans and bots. Use concrete examples like idempotency keys and optimistic locking to show depth.
Ask about expected ticket volume, user roles, bot behavior, and reporting needs. Define functional and non-functional requirements.
Propose a schema for tickets, users, roles, audit logs, and notifications. Choose a database (e.g., relational for consistency) and discuss indexing for search.
Design RESTful APIs for ticket CRUD, with idempotency keys for creation. Include endpoints for search, filtering, and reporting.
Use optimistic locking (versioning) for updates. Define state transitions and enforce RBAC for each action.
Design background jobs for notifications and monthly reports. Discuss scaling with sharding, caching, and async processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.