rate limiting

Both tools read logs and ban addresses after the fact. Knowing what that design can and cannot see is the whole answer, and most candidates call it an authentication control.

A 2 Tbps flood and one expensive API call per second are both denial of service. The answer that scores matches each attack to the place where it can be stopped, and knows which ones only a provider can absorb.

Work through tenant, role, object and field checks, then extend the review to bulk exports, permission changes and resource budgets with executable acceptance cases.

Small requests that trigger large work are the cheapest denial of service. The scored answer finds the multipliers, bounds every one, and adds the AI-era ones.

GraphQL lets clients ask for exactly what they want, which includes things you did not mean to give them and queries that cost a thousand times a normal one. The scored answer moves controls to where GraphQL actually decides things.

An AI request can cost a thousand times more than a web request, so volumetric thinking alone fails. The scored answer layers network, request and cost controls, and protects the GPU tier from ever seeing junk.

An easy question with a revealing follow-up. The answer that scores limits cost at every layer, including the agent loop, not just requests per minute.

Requests per minute is the wrong unit when one request can cost ten thousand tokens. The interview checks the bucket arithmetic, the unknown output length, and what happens when two gateway nodes share one budget.

Bound work where it is dispatched, account for concurrent branches and retries, and keep uncertain operations reserved. Choose limits from workload evidence and operational capacity, then preserve useful progress when a bound is reached.