Load test against a realistic model of the campaign traffic to find the actual bottleneck, which is usually the database or a third party dependency rather than the application tier. Raise capacity and account limits ahead of time, since autoscaling reacts too slowly for a spike, pre warm caches and connection pools, and put rate limiting and graceful degradation in place so overload sheds load rather than collapsing. Agree what you will turn off if it goes wrong.
Why interviewers ask this
The interviewer wants to see planning rather than optimism. They look for load testing to find the real constraint, awareness that autoscaling has a lag and quotas have ceilings, and a degradation plan, because promising that everything will hold is not credible. The best answers include a rehearsed decision about what gets sacrificed to keep checkout or signup working.
How to structure your answer
- Model the expected traffic shape, not just the peak number.
- Load test to find the real bottleneck before scaling anything.
- Pre scale, raise quotas, and warm caches because autoscaling lags.
- Plan degradation, rate limits, and a staffed watch window.
Example answer
First I want the shape, not just the multiple. Twenty times spread over a day is a different problem from twenty times arriving in ninety seconds when an email goes out. Then I load test with a realistic mix of requests against a production sized environment, because the bottleneck is almost never the web tier, it is database connections, a lock on a hot row, or a third party API with its own rate limit that we do not control. Whatever the test surfaces gets fixed or capacity gets added. Ahead of the event I scale out manually rather than trusting autoscaling, because scaling reacts on a several minute lag and the spike will be over or the site will be down by then, and I check account level quotas because that is a classic wall to hit at the worst moment. Caches and connection pools get warmed. Then the degradation plan: rate limit per client, make non essential features like recommendations fail soft, and agree in advance what we switch off to protect checkout. And somebody is watching live, with the rollback path already agreed.
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See how it worksFollow-up questions to expect
- What would you do if the bottleneck turned out to be a third party API?
- How do you load test without affecting real customers?
- What would you monitor live during the campaign window?
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