技术面试英语高频表达

I've spent the last five years building backend systems, mostly around APIs, data pipelines, and reliability.

Before I jump into a solution, I'd like to clarify the scope and the main constraints.

For this system, are we optimizing mainly for latency, throughput, or cost?

My first thought is a brute-force approach, just to establish a correct baseline.

Let me dry-run this on a small example to make sure the transitions actually hold.

I'm a bit stuck on the next step. Could you give me a small nudge rather than the full solution?

I just spotted a bug in my logic. Let me fix that before I build on top of it.

A cache would cut read latency, but we'd need a clear invalidation strategy to keep the data trustworthy.

I'd put a queue between these services so traffic spikes don't directly overwhelm the downstream system.

When we had a production incident, I took ownership of the response, coordinated the fix, and wrote the follow-up.

Instead of debating in abstract, I built a small prototype so the team could react to something concrete.

For an interview solution, this is enough, but in production I'd add validation, monitoring, and failure handling.