Amazon DevOps Engineer Interview Questions

“I would release in waves. The first wave is a few hosts in one quiet region, taking a small share of traffic. I compare their error rate and latency against hosts on the old version and wait long enough for slow problems like memory leaks to appear. If healthy, the release spreads through that region, then to the next, leaving the busiest regions for last. Each stage has an automatic rollback when the signals go bad, so nobody has to decide at night. Database changes go out separately and stay compatible with both versions, so rolling back the code is always safe.”

“Automatic scaling reacts after load rises, and new servers need time to start, warm up and join. When traffic jumps in seconds at a sale start, that gap is where customers see errors. So I would forecast the peak from earlier sales, add a margin, and raise the minimum capacity before the start. I would load test the whole path, because the web tier may scale while the database or a downstream team's service does not. I would also check account limits and connection limits in advance. After the peak passes, capacity steps down slowly, in case a second wave arrives.”

“I would page only on what customers feel: the share of checkouts that fail, how slow the slowest requests are, and a drop in orders compared with the usual level for that hour. Signals about causes, like processor use or disk space, belong on dashboards or as tickets, since they often rise without any harm. Thresholds come from real history and must hold for a few minutes, to skip short spikes. Each alarm links to a runbook. Every week I would review the pages that fired, and anything that needed no action gets tuned or removed, because ignored alarms are dangerous.”

“An emergency change by hand is sometimes right, but it must not stay hidden. I would use the audit log and a plan run of our infrastructure code to see exactly what differs. Then I would decide with the team whether the change was a good fix or a quick patch. If good, I write it into the code and have it reviewed, so the next deployment does not quietly undo it. If not, I apply the code to remove it at a safe time. I would also add drift detection that runs daily, and a simple way to log manual changes during incidents.”

“At my previous company I led an incident where orders stopped flowing to the warehouse system for your result. I took the coordinator role, asked one engineer to investigate and another to handle updates to support, and kept a timeline as we went. We restored service by failing over a message broker. In the review I asked why several times and found two causes: a disk alarm that paged nobody, and a failover that had never been practiced. We fixed the alarm routing and started failover drills each quarter. I tracked both actions until they closed, since reviews without follow-up change nothing.”

“On my team, replacing an unhealthy server took about eleven manual steps, and we did it several times a week. People sometimes skipped the step that drained traffic first. I wrote the steps down, then automated them one at a time, starting with the checks. The tool refused to act on more than one server at once, confirmed the rest of the group was healthy first, and stopped if anything looked odd. For the first weeks it ran in a mode that only printed what it would do. After we trusted it, your result. I also kept the manual runbook current as a backup.”

“The risk now is losing the healthy data centres to overload, so I protect them first. I would check how close they are to their limits and turn off optional features, such as recommendations, so search and checkout keep working. If needed, I would limit the rate of less important traffic. At the same time I would add capacity in the healthy sites. When the failed site has power again, I bring it back gradually with caches warmed, not all at once. In the review, I would ask why losing one site pushed the others so close to the edge.”

“I enjoy operations because success is quiet: a release nobody noticed, a peak that passed without an alarm. Here the systems carry a store that is always open, and cloud services that other companies run their own products on, so an outage spreads far beyond one site. That raises the standard for every deployment and every alarm, and I want to work at that standard. In my current role I built your project and learned how much careful design hides inside a safe release. I would like to learn how teams here prepare for peaks and practice failure before it happens.”