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Kubernetes Training — Topic-Wise Voice + Gemini AI Interviews
Choose any syllabus topic below to launch a live spoken technical interview (max 2 AI interviews per topic per day). Ready to master architecture, debugging, and live deployment directly under a seasoned Industry Expert? Enroll in the complete program (with 244 AI Interview rounds, Live Client Production Project & Verified Internship Letter) for just ₹6,499!
All Modules & Topics in Kubernetes Training
Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability
Purpose: To understand and apply Kubernetes Overview — Declarative Desired State, Level-Triggered Reconciliation & Cloud-Native Portability in real Kubernetes systems — specifically: Why container schedulers superseded Snowflake VMs, the Kubernetes API resource model (`apiVersion`, `kind`, `metadata`, `spec` vs cluster-managed `status`), self-healing control loops, bin-packing efficiency, and CNCF portability across EKS, GKE, AKS, and bare metal, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet`
Purpose: To understand and apply Kubernetes Architecture — `kube-apiserver`, Raft `etcd`, Scheduler, Controller Manager & `kubelet` in real Kubernetes systems — specifically: End-to-end Pod creation flow: `kubectl` -> `kube-apiserver` (AuthN -> AuthZ -> Admission Webhooks -> `etcd` commit) -> `kube-scheduler` (Filtering & Scoring nodes -> Binding) -> target node `kubelet` -> `containerd` CRI + CNI IP allocation, so your code stays reliable, fast, and easy to maintain in production.
`kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug`
Purpose: To understand and apply `kubectl` Mastery — Declarative `apply`, Server-Side Dry-Run, JSONPath & Ephemeral `kubectl debug` in real Kubernetes systems — specifically: kubeconfig contexts (`clusters`, `users`, `contexts`), why `kubectl apply` three-way merge / Server-Side Apply beats imperative `create`/`replace`, validating manifests via `--dry-run=server`, extracting fields with `-o jsonpath`, and debugging distroless Pods via `kubectl debug`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes
Purpose: To understand and apply Kubernetes Pods — Shared Network Namespace, Init/Sidecar Lifecycle, Probes & QoS Classes in real Kubernetes systems — specifically: Why the Pod is the atomic unit of scheduling: the `pause` infra container holding the shared Pod IP/`localhost` port space, sequential `initContainers`, `startupProbe` vs `readinessProbe` vs `livenessProbe`, graceful `SIGTERM` (`preStop` + `terminationGracePeriodSeconds`), and OOMKiller QoS tiers, so your code stays reliable, fast, and easy to maintain in production.
Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection
Purpose: To understand and apply Labels, Annotations & Selectors — Loose Coupling, Set-Based `matchExpressions` & Node Selection in real Kubernetes systems — specifically: How Kubernetes controllers and Services dynamically discover Pods solely via key-value `labels` (never hardcoded IPs!), standard `app.kubernetes.io/*` taxonomy, equality (`=`, `!=`) vs set-based (`in`, `notin`, `exists`) selectors, and why `annotations` hold non-queryable metadata, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange`
Purpose: To understand and apply Kubernetes Namespaces — Scope Boundaries, CoreDNS FQDN, `ResourceQuota` & `LimitRange` in real Kubernetes systems — specifically: Virtual sub-clusters inside a physical cluster: namespaced (`Pod`, `Service`, `Secret`) vs cluster-scoped (`Node`, `PersistentVolume`, `StorageClass`, `ClusterRole`) objects, cross-namespace CoreDNS resolution (`<svc>.<ns>.svc.cluster.local`), and quota enforcement, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging
Purpose: To understand and apply Kubernetes ReplicaSets — Desired Replica Reconciliation, `ownerReferences` & Pod Isolation Debugging in real Kubernetes systems — specifically: How the `ReplicaSet` controller continuously compares matching active Pods against `spec.replicas`, `metadata.ownerReferences` garbage collection (`Cascade=background/foreground/orphan`), and isolating a misbehaving Pod live in production by removing its selector label, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo`
Purpose: To understand and apply Kubernetes Deployments — Zero-Downtime `RollingUpdate`, `maxSurge`/`maxUnavailable` & `rollout undo` in real Kubernetes systems — specifically: How `Deployment` orchestrates old and new `ReplicaSet`s during `RollingUpdate` vs `Recreate`, `maxSurge` & `maxUnavailable` math, `minReadySeconds`, `progressDeadlineSeconds`, `revisionHistoryLimit`, and instant rollback when a bad build fails readiness checks, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s
Purpose: To understand and apply Kubernetes Services — `ClusterIP`, `NodePort`, `LoadBalancer`, Headless DNS & `EndpointSlice`s in real Kubernetes systems — specifically: Why ephemeral Pod IPs necessitate stable virtual Service IPs (`ClusterIP`), mapping `port` -> named `targetPort`, `EndpointSlice` scalability (100 endpoints per slice vs monolithic `Endpoints`), Headless `clusterIP: None` returning direct Pod A/AAAA records, and Ingress / Gateway API L7 routing, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules
Purpose: To understand and apply Kubernetes Network Policies — CNI Enforcement, Default-Deny Posture & DNS Egress Rules in real Kubernetes systems — specifically: How Kubernetes defaults to a flat allow-all network, why `NetworkPolicy` requires a policy-capable CNI (Calico, Cilium, Weave — NOT basic `flannel` alone!), additive union semantics of multiple policies, `namespaceSelector` + `podSelector` AND vs OR YAML list traps, and CoreDNS egress rules, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats
Purpose: To understand and apply Kubernetes Volumes — Ephemeral `emptyDir`, `tmpfs` Memory, Projected Volumes & `subPath` Caveats in real Kubernetes systems — specifically: Why writable container overlay layers vanish on container restart whereas Pod `emptyDir` survives container crashes for the lifetime of the Pod, RAM-backed `emptyDir.medium: Memory`, `projected` volumes combining `ConfigMap` + `Secret` + `serviceAccountToken`, and why `hostPath` is banned in restricted security policies, so your code stays reliable, fast, and easy to maintain in production.
Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer`
Purpose: To understand and apply Persistent Storage & PVCs — CSI `StorageClass`, `AccessModes`, `Retain` vs `Delete` & `WaitForFirstConsumer` in real Kubernetes systems — specifically: Decoupling developer storage requests (`PersistentVolumeClaim`) from cluster infrastructure (`PersistentVolume` + CSI `StorageClass`), block `ReadWriteOnce` (`RWO`) vs shared NFS/EFS `ReadWriteMany` (`RWX`), `reclaimPolicy: Retain` vs `Delete`, and `WaitForFirstConsumer` multi-AZ scheduling, so your code stays reliable, fast, and easy to maintain in production.
Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates`
Purpose: To understand and apply Stateful Applications & `StatefulSet` — Sticky Ordinals, Headless Peer DNS & `volumeClaimTemplates` in real Kubernetes systems — specifically: Why clustered databases/brokers cannot share a single `Deployment` PVC, how `StatefulSet` guarantees stable ordinal Pod names (`kafka-0`, `kafka-1`), predictable peer DNS (`kafka-0.kafka-hl.ns.svc.cluster.local`), dedicated per-ordinal PVCs via `volumeClaimTemplates`, and `OrderedReady` vs `Parallel` lifecycle, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync
Purpose: To understand and apply Kubernetes Secrets — Base64 != Encryption, `etcd` KMS Encryption at Rest, `immutable: true` & CSI Vault Sync in real Kubernetes systems — specifically: Why standard `Secret` `data` is merely Base64-encoded (NOT encrypted by default in `etcd`!), enabling `EncryptionConfiguration` with cloud KMS v2 providers, `Opaque` vs `kubernetes.io/tls` vs `dockerconfigjson` types, setting `immutable: true` to cut kubelet watch load, and External Secrets Operator, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup
Purpose: To understand and apply Kubernetes Jobs & CronJobs — `completions`, `parallelism`, `concurrencyPolicy` & TTL Cleanup in real Kubernetes systems — specifically: Running finite run-to-completion tasks (`restartPolicy: OnFailure | Never`), work-queue vs `completionMode: Indexed` parallel Jobs, bounding retries (`backoffLimit`, `activeDeadlineSeconds`), auto-pruning finished Pods (`ttlSecondsAfterFinished`), and `CronJob` `Allow` vs `Forbid` vs `Replace` overlap rules, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i`
Purpose: To understand and apply Kubernetes RBAC — `Role` vs `ClusterRole`, `ServiceAccount` Tokens, Privilege Escalation & `auth can-i` in real Kubernetes systems — specifically: Additive allow-only authorization: Subjects (`User`, `Group`, `ServiceAccount`), Verbs (`get`, `list`, `watch`, `create`, `patch`, `delete`), reusing a `ClusterRole` inside a single namespace via a `RoleBinding`, time-bound projected `TokenRequest` JWTs, and verifying access with `kubectl auth can-i --as`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling
Purpose: To understand and apply Kubernetes Autoscaling — `HPA v2` Stabilization Windows, VPA, KEDA & Karpenter Node Autoscaling in real Kubernetes systems — specifically: The 3 autoscaling dimensions: Horizontal Pod Autoscaler (`HPA v2`: `desiredReplicas = ceil[currentReplicas * (currentMetric / desiredMetric)]`), Event-Driven `KEDA` scaling to/from zero on Kafka/SQS lag, Vertical Pod Autoscaler (`VPA`), and Cluster Autoscaler / Karpenter provisioning EC2/cloud nodes for `Pending` Pods, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks
Purpose: To understand and apply Kubernetes Helm 3 — Charts, Go Templates (`values.yaml`), OCI Registries & `--atomic` Rollbacks in real Kubernetes systems — specifically: Why Helm 3 eliminated Tiller (pure client-side library obeying user RBAC!), Chart structure (`Chart.yaml`, `values.yaml`, `templates/`, `_helpers.tpl`), `sha256sum` ConfigMap checksum annotations to trigger Deployment rollouts automatically, OCI chart registries, and `helm upgrade --install --atomic`, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector
Purpose: To understand and apply Kubernetes Monitoring — `cAdvisor` vs `kube-state-metrics`, Prometheus `ServiceMonitor` & OTel Collector in real Kubernetes systems — specifically: The cloud-native observability triad: `cAdvisor` (embedded in `kubelet` for container CPU throttling `container_cpu_cfs_throttled_periods_total` & RSS memory), `kube-state-metrics` (API object state: `CrashLoopBackOff`, `Pending` PVCs, `Deployment` replica mismatches), DaemonSet log shipping, and OpenTelemetry Collector, so your code stays reliable, fast, and easy to maintain in production.
Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery
Purpose: To understand and apply Kubernetes GitOps CI/CD — Push vs Pull (`ArgoCD` / `Flux`), Image Digest Pinning & Progressive Delivery in real Kubernetes systems — specifically: Separating CI (test, Trivy scan, Cosign sign, push immutable image digest, open PR updating GitOps manifest repo) from pull-based CD (`ArgoCD` running inside the cluster reconciling Git -> cluster with `selfHeal` & `prune`), plus Argo Rollouts Canary / Blue-Green progressive delivery, so your code stays reliable, fast, and easy to maintain in production.
Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute)
Purpose: To understand and apply Ingress Controllers & Modern Kubernetes Gateway API (HTTPRoute) in real Kubernetes systems — specifically: Route multi-domain HTTPS traffic with canary header/weight splitting and cert-manager TLS, so your code stays reliable, fast, and easy to maintain in production.
Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation)
Purpose: To understand and apply Default-Deny NetworkPolicies (Ingress + Egress Pod Isolation) in real Kubernetes systems — specifically: Ensure a compromised frontend pod can never connect directly to database pods, so your code stays reliable, fast, and easy to maintain in production.
CoreDNS Service Discovery, Headless Services & Topology-Aware Routing
Purpose: To understand and apply CoreDNS Service Discovery, Headless Services & Topology-Aware Routing in real Kubernetes systems — specifically: Resolve internal cluster DNS cleanly and keep pod traffic inside the same cloud AZ, so your code stays reliable, fast, and easy to maintain in production.
Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices
Purpose: To understand and apply Debugging Packet Drops, Ephemeral Debug Containers & EndpointsSlices in real Kubernetes systems — specifically: Diagnose 502/504 cluster networking mysteries rapidly with kubectl debug, so your code stays reliable, fast, and easy to maintain in production.
Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens
Purpose: To understand and apply Roles, ClusterRoles, ServiceAccounts & Disabling Auto-Mounted Tokens in real Kubernetes systems — specifically: Enforce strict per-team and per-microservice API server permissions, so your code stays reliable, fast, and easy to maintain in production.
Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS
Purpose: To understand and apply Pod Security Admission (Restricted Profile), SecurityContext & ReadOnlyRootFS in real Kubernetes systems — specifically: Block privileged containers, root UIDs, and Linux capability escalations cluster-wide, so your code stays reliable, fast, and easy to maintain in production.
External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption
Purpose: To understand and apply External Secrets Operator (AWS Secrets Manager / Vault) & KMS Encryption in real Kubernetes systems — specifically: Sync rotated cloud secrets into pods securely without committing base64 YAML secrets to Git, so your code stays reliable, fast, and easy to maintain in production.
IRSA / Workload Identity Federation for Cloud SDK Access
Purpose: To understand and apply IRSA / Workload Identity Federation for Cloud SDK Access in real Kubernetes systems — specifically: Grant pods fine-grained S3/SQS access via OIDC tokens with zero static cloud keys, so your code stays reliable, fast, and easy to maintain in production.
Authoring Reusable Helm Charts, Values Schema Validation & Hooks
Purpose: To understand and apply Authoring Reusable Helm Charts, Values Schema Validation & Hooks in real Kubernetes systems — specifically: Template standardized microservice deployments across 20+ engineering squads, so your code stays reliable, fast, and easy to maintain in production.
Kustomize Base + Dev/Stage/Prod Patch Overlays
Purpose: To understand and apply Kustomize Base + Dev/Stage/Prod Patch Overlays in real Kubernetes systems — specifically: Manage environment replica/resource differences cleanly without copy-pasting YAML, so your code stays reliable, fast, and easy to maintain in production.
Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD
Purpose: To understand and apply Declarative GitOps Sync, Drift Self-Healing & App-of-Apps in ArgoCD in real Kubernetes systems — specifically: Make Git the single auditable source of truth with 1-click rollbacks, so your code stays reliable, fast, and easy to maintain in production.
Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts)
Purpose: To understand and apply Blue/Green & Automated Metric-Analysis Canary Releases (Argo Rollouts) in real Kubernetes systems — specifically: Auto-abort a rollout within 30 seconds if Prometheus 5xx error rates spike, so your code stays reliable, fast, and easy to maintain in production.
Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention
Purpose: To understand and apply Requests vs Limits QoS Classes, VPA & CPU-Throttling Prevention in real Kubernetes systems — specifically: Right-size pod memory/CPU scientifically and prevent OOMKilled evictions, so your code stays reliable, fast, and easy to maintain in production.
HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter
Purpose: To understand and apply HPA + Event-Driven KEDA (Scale on Kafka Lag / SQS Depth) & Karpenter in real Kubernetes systems — specifically: Scale pods on real queue backlog and provision right-sized spot/on-demand nodes in 40s, so your code stays reliable, fast, and easy to maintain in production.
PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks
Purpose: To understand and apply PodDisruptionBudgets, TopologySpreadConstraints & Graceful preStop Hooks in real Kubernetes systems — specifically: Survive AZ outages and node upgrades with zero user-visible downtime, so your code stays reliable, fast, and easy to maintain in production.
CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting
Purpose: To understand and apply CSI Persistent Storage Snapshots (Velero) & kube-prometheus-stack Alerting in real Kubernetes systems — specifically: Back up stateful volumes/cluster manifests and monitor golden SRE signals, so your code stays reliable, fast, and easy to maintain in production.
Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems
Purpose: To understand and apply Pod Security Standards (Restricted), Non-Root SecurityContexts & Read-Only Root Filesystems in real Kubernetes systems — specifically: Block container privilege escalation and enforce least-privilege RBAC ServiceAccounts, so your code stays reliable, fast, and easy to maintain in production.
Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync
Purpose: To understand and apply Default-Deny NetworkPolicies, TLS Gateway API / Ingress & External Secrets Sync in real Kubernetes systems — specifically: Isolate east-west pod traffic and inject rotated cloud secrets without plaintext YAML, so your code stays reliable, fast, and easy to maintain in production.
Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets
Purpose: To understand and apply Requests/Limits Right-Sizing, HPA/KEDA Event Autoscaling & PodDisruptionBudgets in real Kubernetes systems — specifically: Scale pods smoothly on CPU or queue depth while guaranteeing zero downtime during node drains, so your code stays reliable, fast, and easy to maintain in production.
GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts
Purpose: To understand and apply GitOps Sync Loops with Argo CD, Helm Values Hygiene & Canary/Blue-Green Rollouts in real Kubernetes systems — specifically: Promote audited cluster changes automatically from Git with instant 1-click rollback, so your code stays reliable, fast, and easy to maintain in production.
Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures
Purpose: To understand and apply Systematic Triage of CrashLoopBackOff, ImagePullBackOff, OOMKilled & CoreDNS Failures in real Kubernetes systems — specifically: Debug broken pods, PVCs, endpoints, and CNI networking rapidly using kubectl & events, so your code stays reliable, fast, and easy to maintain in production.
Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews
Purpose: To understand and apply Multi-AZ Topology Spread Constraints & Production Kubernetes Interviews in real Kubernetes systems — specifically: Architect highly available stateful and stateless clusters and pass hands-on K8s scenarios, so your code stays reliable, fast, and easy to maintain in production.