Netflix DevOps Engineer Interview Questions

“We believed our service would survive losing one server, but nobody had tried. I proposed a test in working hours, with the team watching. We agreed on limits first: one server only, a small share of traffic, and a clear signal to stop. I shut the server down. Traffic moved as planned, but a background job that lived only on that server stopped quietly, and no alert fired. That was the real finding. We moved the job to run on any server and added an alert. The result was your result. We now repeat the test regularly.”

“We released a change in the evening, which was our busy time. A few minutes later, errors on one endpoint rose a little. It was small, and someone suggested waiting. We had agreed before the release that any rise in errors on that path meant rollback, so I rolled back and posted a short message to the team. It took a few minutes and errors returned to normal. The next morning we found a missing setting in one environment. I was glad the rule was set in advance, because in the moment it is tempting to hope. The outcome was your result.”

“Each server reports its health and load, such as open connections and disk problems. When a viewer presses play, the steering service ranks servers that hold the file by how close they are on the network, then removes any that are unhealthy or near their limit. I would give the player a short ordered list, so it can switch by itself if the first choice fails during the film. One risk is moving everyone from a failed server onto its neighbour and overloading that too. So traffic should spread across several servers, even if some are a little further away.”

“I would run the new version on a small group of servers next to a same-sized group running the old version, both started fresh, so the comparison is fair. The first signals I compare are what the viewer feels: how many play attempts fail, how long the first picture takes, and how often playback stops to load. Then the service signals: error rate, response time and memory. The stop rules are written before the release, and the pipeline applies them without waiting for a person. I would also let it run long enough to catch slow problems like a memory leak.”

“The move only works if it was prepared. Each region must hold a copy of the data that viewers need, and be able to grow quickly. When a region fails, I first scale up the regions that will receive the traffic, because they need time to start servers and warm caches. Then I shift traffic in steps, watching errors in the receiving regions after each step. Services that are not vital to playback can be reduced to free capacity. Most important, I would run this move on a schedule when nothing is wrong, so the real day is a practiced routine.”

“I would choose one signal that stands for the viewer: successful play starts per second. It follows a steady daily pattern, so I compare the current value with what is normal for that hour and that day of the week. A sharp drop below the expected band pages the engineer on call. I would split it by region and device type, since a failure on one kind of television can hide in the total. Busy processors and full disks still get recorded, but they go to dashboards and tickets. They help explain an incident. They should not wake anyone by themselves.”

“First I would confirm the pattern in the data: one provider, one country, all devices. That points to the network path and not to our apps. I would check the servers that serve this provider for errors, load and recent changes. If one is unhealthy, I take it out of rotation. If our servers look fine, the path between us and the provider may be congested, so I would try steering a small share of those viewers to another location and see whether it improves. Meanwhile I contact the provider's network team with measurements and times, and keep a written timeline.”

“What attracts me is that there is no maintenance window. When it is night in one country it is the evening peak in another, so every change has to be safe while people watch. That forces good habits: small releases, automatic checks, and practicing failure before it happens. In my current role I built your project, and I learned how much calmer incidents are when recovery has been rehearsed. I want to work where that is taken seriously at very large scale, and to learn how video delivery works close to the viewer, which is new to me.”