Technical Interview Listening

[Technical interview — Backend role, mid-level. Interviewer and candidate.]
Interviewer: "Let's start with a system design question. How would you design a URL shortener like bit.ly?"
Candidate: "Sure. Before I dive in — can I ask a few clarifying questions to understand the scale?"
Interviewer: "Of course."
Candidate: "OK. So — are we designing for read-heavy or write-heavy traffic? Are we expecting, say, a thousand URL shortenings per day or a million? And do we need analytics, like click tracking?"
Interviewer: "Good questions. Let's say read-heavy — one million reads per day, a hundred thousand writes. No analytics for now."
Candidate: "Perfect. So the core challenge is generating short unique codes and serving redirects quickly. I'd start with a base62 encoding of an auto-incremented ID — that gives us short, unique alphanumeric codes. For the redirect latency, we'd cache popular URLs in Redis with a TTL that refreshes on access. For storage, a simple key-value store works — DynamoDB or Redis itself. One thing to think about is hash collision avoidance and what happens when the counter overflows, but at this scale we're fine."

[Technical interview — Frontend role. Behavioural question.]
Interviewer: "Tell me about a time you had to make a difficult technical decision under pressure."
Candidate: "Sure — this was about six months ago. We were three days before a product launch and discovered our dashboard had a performance issue — tables with ten thousand rows were taking over eight seconds to render. That's obviously unusable."
Interviewer: "What did you do?"
Candidate: "I had to choose between two options quickly. Option one: virtualise the list — only render the rows visible in the viewport. Option two: add server-side pagination — send the data in pages of fifty. Virtualisation would keep the UX seamless but required a library we hadn't evaluated. Pagination was safer technically but would add complexity to the backend API and change the UX flow."
Interviewer: "Which did you choose?"
Candidate: "I went with virtualisation using react-window. I scoped the implementation to two days, wrote a proof-of-concept in four hours, and the performance went from eight seconds to under a hundred milliseconds. The trade-off was adding a dependency we haven't fully stress-tested — I flagged that for a post-launch review."
Interviewer: "Good. What would you do differently?"
Candidate: "Honestly — I'd have caught it earlier. We should have load-tested the UI with production-scale data, not just ten rows in dev."

[Technical interview — DevOps / SRE role. Live scenario question.]
Interviewer: "You get paged at 2 AM. The monitoring dashboard shows the API latency has spiked from 50ms to 4 seconds. Error rate is at 12%. Walk me through your response."
Candidate: "OK. First — is this affecting users right now? Yes, so time matters. My first priority is to stop the bleeding, not to find the root cause immediately. I'd look at recent deployments — did anyone push to production in the last hour? That's the most common cause of sudden latency spikes."
Interviewer: "Yes, a deployment went out at 1 AM."
Candidate: "That's my primary hypothesis. I'd check if the deployment included any database migrations or dependency updates. Simultaneously I'd look at infrastructure metrics — CPU, memory, database connection pool. If the deployment is the cause, I'd immediately roll back and confirm if latency normalises."
Interviewer: "Rollback confirmed. Latency is back to 50ms."
Candidate: "Good. Now I open a post-mortem doc. I document the timeline: deployment at 1 AM, spike starts at 1:15, paged at 2 AM, rollback at 2:07, resolved at 2:08. Then I hand off to the morning team with the doc and a note that this deployment needs root cause analysis before re-deploying."
Interviewer: "What would you look for in the root cause analysis?"
Candidate: "I'd run the deployment in a staging environment with production-scale load and compare the SQL query plan before and after — my guess is a missing index on a new or modified query."