PCI-DSS Serverless Payments on GCP: Confidential VMs, CEKM & Binary Authorization

AWS Network Firewall in a centralized inspection VPC is the right answer when security hands you a ticket to block unexpected egress — here's how to design it without taking down production at 2am.You'll learn:Why the firewall lives in one centralized inspection VPC behind AWS Transit Gateway, not deployed per spoke VPC — and how the route tables enforce thatHow stateful domain-list rules use TLS SNI to allow *.github.com and *.pypi.org while dropping everything else on 443 — without decrypting trafficThe encrypted client hello (ECH) blind spot: why SNI-based rules can silently stop matching and what you need as a backstopHow to roll out in alert/count mode first, capture a full week of egress patterns to CloudWatch, and flip rule groups to drop one at a timePricing gotcha: endpoint hours per AZ plus data processed means centralized inspection is cheaper, but you must size for combined peak across all VPCsKeywords: AWS Network Firewall interview questions, TLS SNI egress filtering, centralized inspection VPC, cloud security engineer interview, data exfiltration AWS🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud▶ Daily 30-second interview drills: DevOps Interview Cloud on YouTubeTranscriptSecurity just sent you a ticket. Block all unexpected egress across every VPC. No more instances calling out to random IPs on the internet. Sounds simple until you realize your build servers pull from package registries, your app calls three SaaS APIs, and nobody has a full list of what's actually supposed to be leaving the network. Ship the wrong rule set and you either miss real exfiltration paths, or you break a deploy at two in the morning and get paged for it.This question shows up in senior DevOps and cloud security interviews because it tests three things at once. Can you design for centralized inspection instead of bolting a firewall onto every VPC. Do you understand the tradeoff between IP-based and domain-based filtering. And do you have a rollout process that doesn't nuke production traffic on day one. Anyone can say "add a firewall." Fewer people can explain why it goes in one place, and how you prove it's safe before you flip it to blocking mode.Start with the mental model. In a multi-VPC setup, you do not want AWS Network Firewall deployed separately in every spoke VPC. That's operationally painful, it's expensive, and every team ends up writing slightly different rules. The standard pattern is a centralized inspection VPC, sitting between your spoke VPCs and the internet, usually paired with AWS Transit Gateway. Every spoke VPC routes its default egress through the transit gateway to the inspection VPC. The firewall endpoints live there, traffic gets inspected once, using one shared rule set, and then it's routed out through a NAT gateway to the internet.The firewall itself has two kinds of rules. Stateless rules operate on packet headers, source and destination IP, ports, protocol. They're fast, but they don't know anything about the content or the destination domain. Stateful rules track connections and can inspect protocol details, and this is where domain filtering comes in. AWS Network Firewall supports domain-based stateful rules using the HOME_NET and TLS SNI, the Server Name Indication field. Since most traffic is encrypted, the firewall reads the SNI in the TLS handshake to figure out what domain a connection is headed to, without decrypting the actual payload.Here's the walkthrough security actually wants. Instead of writing rules like "allow this IP range," which breaks the moment a SaaS vendor rotates their infrastructure behind a content delivery network, you write domain allow lists. Something like: allow egress to star dot amazonaws dot com, star dot github dot com, star dot pypi dot org, your internal artifact registry domain, and your approved SaaS endpoints like star dot datadoghq dot com. Everything else on port 443 and port 80 gets dropped by a default

A real-world CoreDNS latency incident at high query volume reveals how ndots and Kubernetes search domains silently multiply DNS lookups into a full-blown query storm.You'll learn:Why a single external hostname like api.stripe.com generates 4 upstream queries under Kubernetes' default ndots:5 — and how that 4x amplifier compounds at scaleHow to override search behavior per-pod via dnsConfig.ndots, and why a trailing dot in your FQDN collapses four queries into oneTuning the CoreDNS cache plugin: setting denial TTLs high enough that NXDOMAIN responses actually stick, and the staleness tradeoff you must articulate in interviewsWhat the autopath plugin actually does differently — moving search-path resolution server-side — and when it helps versus when it shifts the problemWhy interviewers use this question to separate engineers who've read the docs from those who've debugged a DNS storm at 3amKeywords: CoreDNS tuning, Kubernetes DNS ndots, negative caching NXDOMAIN, CoreDNS cache plugin, Kubernetes networking interview🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud▶ Daily 30-second interview drills: DevOps Interview Cloud on YouTubeTranscriptPicture this. Your cluster is doing something like a hundred thousand requests per second across a few hundred services, and suddenly every outbound call to a third-party API starts timing out intermittently. Nothing in your application changed. No deploy went out. But your p99 latency for anything that touches DNS resolution just tripled, and your CoreDNS pods are pegged at max CPU. This is a real failure pattern, and it almost always traces back to two things: how ndots is configured in every pod's resolver, and how CoreDNS is caching, or failing to cache, negative responses.Interviewers ask about this because it separates people who've read the Kubernetes docs from people who've actually had to explain a production DNS storm to their team. Anyone can say 'CoreDNS handles DNS in the cluster.' Far fewer people can explain why a single hostname lookup can turn into four or five actual queries hitting your DNS servers, and what that does to your query volume when you're already running near capacity. This question tests whether you understand resolution mechanics, not just that a service called CoreDNS exists.Here's the mental model. Every pod in a Kubernetes cluster gets a resolv.conf file, and that file has two important settings: a search list and an ndots value. The search list typically looks like namespace dot svc dot cluster dot local, then svc dot cluster dot local, then cluster dot local, and depending on your cloud provider, maybe an additional domain from the node itself. The ndots value, five by default in Kubernetes, tells the resolver: if the name you're looking up has fewer than five dots in it, don't treat it as fully qualified. Instead, try appending each entry in the search list first, in order, before falling back to treating it as an absolute name.Now think about what that means for a completely ordinary external lookup, something like api dot stripe dot com. That name has two dots. Since two is less than the ndots threshold of five, the resolver assumes it might be a relative name inside your cluster. So it tries api dot stripe dot com dot your-namespace dot svc dot cluster dot local first. That fails, comes back NXDOMAIN. Then it tries api dot stripe dot com dot svc dot cluster dot local. Fails again. Then api dot stripe dot com dot cluster dot local. Fails again. Only on the fourth attempt does it try api dot stripe dot com as an absolute name, with a trailing dot, and finally succeeds.So a single application-level DNS call just became four queries hitting CoreDNS, three of which were guaranteed to fail. Now multiply that across your fleet. If you're doing a hundred thousand real DNS-triggering requests per second, and a meaningful chunk of those are external hostnames going through this same four-query pattern, y

Your Cassandra node is getting evicted at 2 a.m. or your PostgreSQL replica is sitting on 4× the memory it needs — this episode breaks down exactly which autoscaler to reach for and why VPA vs HPA is a staple senior SRE interview question.You'll learn:Why HPA's scale-out model breaks for StatefulSets (token rings, replication slots, unacknowledged messages) and when vertical scaling is the only safe leverVPA's three components — recommender, admission controller, updater — and why update mode auto is the one that pages you at 2 a.m. for stateful workloadsThe safe progression: start in Off mode for two weeks, apply recommendations manually, then consider Initial mode — and why Auto is rarely worth it for anything with persistent stateThe HPA + VPA feedback loop failure: both watching CPU on the same workload, pod count oscillating, resource allocation in chaosThe clean split that actually works: HPA on queue depth (custom metric), VPA managing memory requests — distinct signals, no interferenceKeywords: VPA vs HPA Kubernetes, vertical pod autoscaler stateful workloads, autoscaler SRE interview, HPA VPA conflict, Kubernetes StatefulSet autoscaling🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud▶ Daily 30-second interview drills: DevOps Interview Cloud on YouTubeTranscriptYour stateful app is getting evicted every few hours, or it's sitting on four times the memory it actually needs, and you're not sure which autoscaler to reach for. That's the exact scenario interviewers love, because most candidates only know one half of the answer.Let's set the stage. You have a Cassandra node, a RabbitMQ broker, or a PostgreSQL read replica running in Kubernetes. It's a StatefulSet. Traffic is not uniform. Some days it's busy, some days it's quiet. Someone on your team says "just add HPA" and someone else says "we need VPA." Who's right? That question shows up in senior SRE and platform engineer interviews constantly, because the answer is not obvious and the wrong choice causes real incidents.Interviewers ask this because autoscaling is one of those topics where surface-level knowledge falls apart fast. Saying "HPA scales pods out, VPA scales pods up" is correct but incomplete. The follow-up is always: okay, so which one do you use for a stateful workload, and why? And then: what happens if you use both at the same time? If you can't answer those, you signal that you've only worked with stateless services.Here's the mental model you need. The HPA, the Horizontal Pod Autoscaler, works by changing the number of pod replicas. It watches a metric, CPU or memory or a custom one, and when that metric crosses a threshold it adds or removes pods. That works beautifully for stateless services. Each replica is identical, sessions don't matter, and spinning up a new pod behind a load balancer is invisible to users.Stateful workloads break that assumption. A Cassandra node owns a subset of the token ring. A RabbitMQ broker may hold unacknowledged messages. A PostgreSQL replica has an open replication slot. Adding a new pod doesn't instantly help because the new pod has to join the cluster, sync data, or acquire state before it can carry load. Scaling out fast is often dangerous. Scaling in is even worse because you might be removing a pod that holds data not yet replicated elsewhere.So for stateful workloads, the more useful lever is usually vertical. Give the existing pod more CPU or more memory so it can handle the load without needing a new replica. That's where the VPA, the Vertical Pod Autoscaler, comes in.VPA has three components. The recommender watches historical resource usage and calculates what your requests and limits should be. The admission controller patches those values into new pods at scheduling time. And the updater, this is the dangerous one, can evict running pods so they restart with the new resource values. The key knob is the update mode, and this is a common interview quest

Learn how to write a Kubernetes scheduler extender webhook that restricts GPU pods to nodes with NVLink interconnects. This episode covers the extender contract, KubeSchedulerConfiguration registration, filter and prioritize endpoints, and the latency tradeoffs interviewers probe in senior SRE and platform engineering interviews.Full interview prep guides and scenario walkthroughs: DevOpsInterview.Cloud

What really happens when you run kubectl apply? In Part 1 of this Kubernetes masterclass, we go far beyond basic definitions and trace how Kubernetes works as a distributed, API-driven control system. You will learn how a YAML manifest moves through kubectl, the API server, authentication, authorization, admission, etcd, controllers, the scheduler, kubelet and the container runtime before finally becoming a running Pod. This episode also explains the deeper ideas that make Kubernetes work: