DevOps & Cloud Interview Prep: Real Scenarios & Answers

Epizode

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  AWS Network Firewall: Stop Exfil Without Breaking Egress 09.09.2026 8min

  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
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  CoreDNS at 100K RPS: ndots, Negative Caching, and Autopath 30.08.2026 9min

  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
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  VPA vs HPA for Stateful Workloads: Autoscaler Deep Dive 22.07.2026 10min

  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
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  Kubernetes Scheduler Extenders: Custom Placement Logic 15.07.2026 9min

  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
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  The Kubernetes Machine: From kubectl apply to Running Containers 12.07.2026 54min

  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: Desired state versus observed state Reconciliation loops spec versus status Watches and events Labels and selectors ReplicaSets and Deployments Scheduling decisions Pod lifecycle Owner references, finalizers and garbage collection Server-side apply and field ownership By the end of this episode, you will be able to mentally replay the complete journey from user intent to a healthy running workload—and understand which component is responsible at every step. Mental model: Intent → Store → Observe → Reconcile Full interview prep guides and scenario walkthroughs: DevOpsInterview.Cloud
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  Cluster Autoscaler vs Karpenter: Choosing at 500 Nodes 08.07.2026 10min

  Most engineers assume Karpenter is always the right answer for Kubernetes node autoscaling, but at 500 nodes the tradeoffs around ASG lock-in, provisioner complexity, and migration risk get serious. This episode breaks down when to keep Cluster Autoscaler, when Karpenter wins, and how to articulate both sides clearly in a senior DevOps or SRE interview. Covers real configuration details, scaling latency numbers, and common wrong answers interviewers flag.Full interview prep guides and scenario walkthroughs: DevOpsInterview.Cloud
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  OOMKilled at Scale: Tuning JVM Heap in Kubernetes 05.07.2026 10min

  A Java service keeps getting OOMKilled in Kubernetes even though memory requests look fine on paper. This episode explains why JVM heap defaults ignore container limits, how to set maximum heap size correctly, and what interviewers expect when they probe your understanding of Java memory in containerized environments. Covers Xmx flags, UseContainerSupport, native memory overhead, and the tradeoffs between requests and limits.Full interview prep guides and scenario walkthroughs: DevOpsInterview.Cloud
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  Karpenter Spot Interruption: Fallback & Graceful Drain 04.07.2026 33min

  When AWS fires the 2-minute Spot reclaim notice, Karpenter's interruption queue is the difference between a blip and a batch job disaster — here's exactly how to configure it.You'll learn:How to set karpenter.sh/capacity-type in a NodePool to prefer Spot with automatic On-Demand fallbackThe full interruption flow: SQS queue → cordon → graceful drain → pod rescheduling, all within the 2-minute windowWhy the order of values in the capacity-type array doesn't control selection — Karpenter uses price-capacity optimizationWhen to use strict values: ['spot'] and what happens when capacity dries upWhy Pod Disruption Budgets and gracefulTerminationPeriod are non-negotiable for fault-tolerant batch workloadsKeywords: Karpenter Spot interruption handling, Spot instance fallback on-demand, NodePool capacity type configuration, Kubernetes batch workload cost optimization, Spot 2-minute warning drain🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Canary Analysis for Flink Streaming: Prometheus, Loki & Pyroscope 04.07.2026 18min

  Automated canary analysis for a Flink-based streaming app is a common senior SRE interview scenario — here's how to wire Prometheus, Loki, and Pyroscope into a production-grade rollout strategy.You'll learn:How to define canary success criteria using Prometheus metrics like consumer lag, throughput, and error rate on Flink jobsUsing Loki log queries to surface structured errors in canary vs. baseline deployments side-by-sideContinuous profiling with Pyroscope to catch CPU or memory regressions in the new Flink version before full rolloutHow automated analysis gates work — failing fast vs. baking time — and how to articulate the tradeoff in an interviewStitching observability signals into a single canary decision: pass, fail, or inconclusiveKeywords: canary deployment Flink, automated canary analysis SRE, Prometheus Loki Pyroscope, streaming app observability, DevOps interview questions🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Grafana Mimir Storage: Tiered S3 at 10TB/day 04.07.2026 13min

  Grafana Mimir storage at 10TB/day scale forces real trade-offs — here's how to configure tiered storage to S3 without bleeding cost or tanking query performance.You'll learn:How Mimir's store-gateway and compactor interact with S3-backed object storage at high ingest volumeConfiguring blocks_storage with tiered retention — keeping hot blocks in fast storage while offloading cold blocks to S3 Glacier-compatible tiersTuning compaction schedules and chunk caching (memcached) to reduce S3 GET costs under sustained 10TB/day ingestCommon pitfalls: misconfigured bucket lifecycle policies, compactor overlap errors, and index cache misses killing query latencySizing ruler and alertmanager storage separately so they don't contend with block storage I/OKeywords: Grafana Mimir S3 storage, Mimir tiered storage config, Mimir compactor tuning, metrics storage at scale, Mimir blocks_storage🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  SLO Error Budget Burn Rate: Azure Zone Outage Math 23.06.2026 10min

  If your service has a 99.99% SLO and Azure drops a zone for 15 minutes, here's exactly how to calculate the error budget burn rate before your next SRE interview.You'll learn:How to derive total monthly error budget from a 99.99% SLO (~4.38 minutes/month)Why a 15-minute outage consumes roughly 3.4x your entire monthly budget — and how to show that mathThe burn rate formula interviewers expect: burn rate = error rate / (1 − SLO target)How fast vs. slow burn rates map to alerting windows in Google's SRE workbook approachCommon gotchas: partial zone failures, dependency blame, and how to frame mitigation in your answerKeywords: SLO error budget burn rate, Azure availability zone outage, SRE interview questions, error budget calculation, 99.99 SLO math🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  PCI-DSS Serverless Payments on GCP: Confidential VMs, CEKM & Binary Authorization 23.06.2026 18min

  Designing a PCI-DSS compliant serverless payments architecture on GCP means getting Confidential VMs, Cloud External Key Manager, and Binary Authorization working together — here's how to answer that in a senior interview.You'll learn:How Confidential VMs provide hardware-level memory encryption to satisfy PCI-DSS data-in-use requirementsWhy Cloud External Key Manager (CEKM) lets you hold encryption keys outside GCP's control — and what that means for scope reductionHow Binary Authorization enforces cryptographic attestation so only verified container images reach your payment workloadsThe serverless boundary decisions (Cloud Run vs bare GKE) that affect your Cardholder Data Environment scopeCommon interview gotchas around shared responsibility, audit logging with Cloud Audit Logs, and VPC Service Controls for perimeter defenceKeywords: PCI-DSS GCP architecture, Confidential VMs interview, Cloud External Key Manager, Binary Authorization Cloud Run, serverless payments compliance🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Cross-Account EKS with AWS CDK: VPC Peering and Transit Gateway 23.06.2026 13min

  Deploying EKS clusters across AWS accounts with CDK is a common senior interview scenario — here's how to handle VPC peering, Transit Gateway attachments, and IAM trust policies correctly.You'll learn:How to structure a multi-account CDK app using Stacks across environments with explicit env account/region targetsWhen to use VPC peering vs Transit Gateway for cross-account EKS network connectivity, and the trade-offs at scaleHow to wire up Transit Gateway attachments and route table propagation so worker nodes can reach shared servicesCross-account IAM role assumptions and EKS RBAC config required for cluster access from a management accountCommon CDK gotchas: bootstrap trust policies, asset S3 bucket permissions, and cross-account CFN execution rolesKeywords: cross-account EKS CDK, AWS Transit Gateway EKS, VPC peering Kubernetes, multi-account EKS architecture, AWS CDK EKS interview🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  OpenTelemetry + CloudWatch Logs Insights: Tracing Serverless Apps 21.06.2026 18min

  Correlating OpenTelemetry traces with CloudWatch Logs Insights across Lambda and Step Functions is a common senior interview scenario — here's exactly how to answer it.You'll learn:How to propagate trace context (W3C TraceContext headers) across Lambda invocations and Step Functions state transitions so trace IDs land in your structured logsConfiguring the AWS Distro for OpenTelemetry (ADOT) Lambda layer to auto-instrument functions without cold-start penaltiesWriting CloudWatch Logs Insights queries that join on trace_id to reconstruct an end-to-end execution timeline across servicesWhere correlation breaks — async Step Functions callbacks, missing X-Amzn-Trace-Id propagation, and log sampling mismatchesTrade-offs between ADOT, X-Ray native SDK, and a third-party collector like the OpenTelemetry Collector on FargateKeywords: OpenTelemetry Lambda tracing, CloudWatch Logs Insights trace correlation, ADOT Step Functions, serverless observability interview questions🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Terraform State Splitting: terraform state rm + moved Blocks 21.06.2026 20min

  Splitting a monolithic 4GB Terraform state file into scoped microstates is one of the nastiest live-infrastructure challenges you'll face — here's how to do it without downtime using terraform state rm and moved blocks.You'll learn:Why state files balloon past 4GB and why that breaks plan/apply performanceHow to use terraform state rm to surgically extract resources without destroying themUsing moved blocks to re-home resources into child state backends cleanlySequencing the migration to avoid drift, lock contention, and accidental deletesHow to validate microstate integrity with terraform state list and targeted plans before cutting overKeywords: terraform state splitting, terraform state rm, moved blocks terraform, monorepo to microstate migration, terraform refactor interview🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Monorepo CI at Scale: Bazel Caching for 1,000 Microservices 20.06.2026 20min

  Designing a monorepo CI pipeline that doesn't collapse under 1,000 microservices means getting Bazel remote caching and selective test execution right from the start.You'll learn:How to structure a monorepo CI pipeline so only affected services trigger builds — using Bazel's dependency graph to compute the minimal affected setConfiguring Bazel remote caching (local cache, shared remote cache via gRPC or HTTP) to avoid rebuilding unchanged targets across parallel CI workersSelective testing strategies: combining bazel query with --build_event_stream to identify and run only impacted test targetsCommon failure modes at scale — cache poisoning, overly broad BUILD file dependencies, and flaky remote executor connectionsHow to structure the CI orchestration layer (GitHub Actions, Buildkite, or Tekton) to fan out Bazel shards without thrashing the remote cacheKeywords: monorepo CI pipeline, Bazel remote caching, selective testing microservices, CI at scale DevOps interview, platform engineering build systems🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Azure RBAC with Pulumi: Dynamic Roles from YAML 20.06.2026 17min

  Learn how to generate dynamic Azure RBAC role assignments using Pulumi with YAML-driven definitions — including tag-scoped conditions like restricting storage access to env:prod resources only.You'll learn:How to define custom Azure RBAC roles in YAML and hydrate them through Pulumi's automation layerUsing condition and conditionVersion fields in role assignments to enforce attribute-based access control (ABAC)Scoping storage permissions to resources matching specific tag key/value pairs at assignment timeStructuring Pulumi component resources so YAML definitions stay DRY across multiple environmentsCommon gotchas: condition syntax errors, propagation delays, and principal vs. scope mismatchesKeywords: Azure RBAC Pulumi, dynamic role assignments Azure, Pulumi YAML infrastructure, Azure ABAC tag conditions, custom RBAC roles interview🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Prometheus Cardinality: Cutting 10M Series to 500K for Istio 17.06.2026 22min

  Taming Prometheus cardinality explosion in an Istio service mesh — dropping from 10 million to 500K active series using relabel_configs and recording rules — is exactly the kind of production war story senior SRE interviews dig into.You'll learn:Why Istio telemetry generates cardinality explosions and which high-cardinality labels (source_workload, destination_service, pod IPs) are the usual culpritsHow to use metric_relabel_configs to drop or rewrite labels before series are ingested into TSDB storageWriting recording rules to pre-aggregate high-resolution Istio metrics into lower-cardinality rollupsUsing topk and cardinality analysis queries to identify which metrics are burning your series budgetTrade-offs between dropping labels at scrape time versus aggregating at query time — and why interviewers care about the differenceKeywords: Prometheus cardinality, Istio metrics, relabel_configs, recording rules, TSDB series limit🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Conftest in Argo CD: Block Public S3 Buckets at GitOps Gate 17.06.2026 18min

  A developer pushes a Terraform module with a public S3 bucket — here's exactly how to catch and block it in your Argo CD pipeline using Conftest policy-as-code before it ever reaches production.You'll learn:How Conftest integrates with Argo CD as a pre-sync hook to enforce OPA policies on Terraform plansWriting a Rego rule that flags acl = public-read or block_public_acls = false on aws_s3_bucket resourcesWhere in the GitOps workflow the gate fires — and why admission controllers alone aren't enough for IaC driftHow to surface policy failures as Argo CD sync errors so engineers see the violation before merge, not after deployCommon gotchas: Terraform plan JSON output format, conftest namespace mismatches, and false positives on legacy modulesKeywords: Conftest Argo CD policy, OPA Terraform GitOps, block public S3 bucket IaC, GitOps security controls, Rego policy Terraform plan🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud
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  Terragrunt at Scale: Dependency Graphs, Circular Deps & OCI Versioning 17.06.2026 19min

  Managing a Terragrunt dependency graph across 500+ modules without hitting circular dependencies or version drift is one of the hardest scaling problems in platform engineering.You'll learn:How to map and audit a large Terragrunt dependency graph using terragrunt graph-dependencies and DAG visualisation toolsPatterns for structuring module hierarchies to prevent circular dependencies before they reach CIEnforcing module versioning with OCI registries — why OCI beats Git tags at this scaleHow to segment a 500+ module monorepo into dependency tiers so targeted runs stay fastCommon failure modes: implicit dependencies, missing mock_outputs, and run-all ordering bugsKeywords: Terragrunt dependency graph, Terragrunt at scale, OCI module registry, circular dependencies Terraform, platform engineering IaC🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud