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Your microservices platform has 200+ services across 3 clusters. Users report intermittent 5-second latency spikes affecting checkout. The issue is non-reproducible locally, affects only ~2% of requests, and disappears before you can investigate. Walk through your systematic debugging approach.
How do you configure a GitLab CI/CD pipeline to build container images, update Kubernetes manifests, and execute secure deployments to a Kubernetes cluster using ServiceAccounts?
Why is container orchestration essential when running containerized applications at scale, and what core problems does Kubernetes solve compared to manual container management?
How do you architect a production-ready stateless web application deployment in Kubernetes combining Deployments, HPA, PodDisruptionBudgets, and anti-affinity rules for high availability?
Your platform team must implement zero-trust networking for a financial services application. Requirements: all inter-service communication must be mutually authenticated and encrypted, no pod should communicate with any other pod unless explicitly allowed, east-west traffic must be auditable, and the solution must not require application code changes. Design and implement this.
How does the GitOps pull-based architecture (using ArgoCD or Flux) differ from traditional push-based CI/CD pipelines, and how does it detect and reconcile cluster state drift automatically?
What specific operational problems arise when managing containers manually across multiple hosts, and how does Kubernetes automate self-healing, scaling, and service discovery?
How do the Sidecar, Ambassador, and Adapter multi-container Pod patterns enhance application functionality, and when should you use each pattern in production?
What are the key architectural differences between Sidecar, Ambassador, and Adapter pod patterns, and how do they extend containerized workloads without modifying primary application code?
Explain the architecture, failure modes, and operational best practices for Containers vs. Virtual Machines: Isolation Mechanisms at the Linux Kernel Level in production Kubernetes environments.
Your platform team needs to automate database provisioning. Every time a developer creates a `Database` custom resource, the system should automatically provision a PostgreSQL StatefulSet, a Service, a Secret with credentials, and register the database in your internal service catalog — then clean everything up when the resource is deleted. Build this operator.
Explain the architecture, failure modes, and operational best practices for Debugging Pending Pods: Systematic Troubleshooting for Scheduling Blocks in production Kubernetes environments.
How do Kubernetes NetworkPolicies enforce micro-segmentation and pod traffic isolation, and how do CNI plugins like Calico or Cilium implement these rules at the kernel network layer?
Explain the architecture, failure modes, and operational best practices for CrashLoopBackOff Troubleshooting: Root Cause Analysis for Application Container Crashes in production Kubernetes environments.
Explain the architecture, failure modes, and operational best practices for Docker vs. Kubernetes: Runtime Engines vs. Cluster Orchestrators in production Kubernetes environments.
Your 500-node production cluster is experiencing API server latency spikes. Investigation shows etcd is the bottleneck: `etcd_disk_wal_fsync_duration_seconds` P99 is 150ms (should be <10ms), leader elections are happening frequently, and the etcd database size is approaching 8GB. Walk through your complete etcd performance diagnosis and remediation plan.
How do you design a comprehensive disaster recovery strategy for Kubernetes using etcd snapshots for control plane state and Velero for persistent volume backups?
Explain the architecture, failure modes, and operational best practices for Control Plane vs. Worker Nodes: Key Architecture Components of a Kubernetes Cluster in production Kubernetes environments.
Your company operates in US-East, EU-West, and APAC. Compliance requires EU data to stay in EU (GDPR), US financial data to stay in US (SOX), and APAC to serve as a DR site for both regions. SLA is 99.95% (< 4.4 hours/year downtime). Design a complete multi-cluster strategy.
What kernel mechanisms trigger an OOMKilled exit status for a container, and how do you configure container memory requests and cgroups limits to prevent out-of-memory container crashes?
What step-by-step workflow guarantees zero downtime when upgrading worker nodes and control plane components using `kubeadm`, `cordon`, and `drain`?
Explain the architecture, failure modes, and operational best practices for etcd Architecture: Distributed Key-Value Store for Kubernetes Cluster State in production Kubernetes environments.
How do you construct and deploy a custom Kubernetes Admission Webhook from scratch, and what TLS certificate requirements must be satisfied for the API server to communicate with it securely?
How does an Ingress Controller manage Layer 7 HTTP/HTTPS traffic routing and SSL/TLS termination, and how does it differ from a NodePort or LoadBalancer Service?
Your company runs a production Kubernetes cluster on v1.26. The security team has mandated an upgrade to v1.29 due to a CVE. You have 40 nodes, stateful workloads, and a 99.9% uptime SLA. Walk through your complete upgrade strategy.
Your organization must comply with SOC 2 and PCI-DSS. The security team requires that no container image with a Critical or High CVE reaches production, all Kubernetes manifests must comply with the CIS Kubernetes Benchmark, and secrets must never appear in source code or image layers. Design and implement a complete DevSecOps pipeline.
Explain the architecture, failure modes, and operational best practices for CoreDNS Architecture: In-Cluster DNS Resolution and Service Record Lookup in production Kubernetes environments.
Why does Kubernetes use a Pod—rather than a single container—as its smallest deployable unit, and how do containers inside the same Pod share network and storage namespaces?
What is OPA/Gatekeeper, how does it integrate with Kubernetes Validating Admission Webhooks, and how do ConstraintTemplates define declarative cluster governance policies?
Explain the architecture, failure modes, and operational best practices for Control Plane vs. Worker Nodes: Responsibilities and Component Breakdown in production Kubernetes environments.
How do PersistentVolumes, PersistentVolumeClaims, and StorageClasses decouple storage consumption from underlying cloud storage infrastructure in Kubernetes?
How do you implement an admission policy using OPA/Gatekeeper or Kyverno to enforce that all containers deployed to a cluster originate exclusively from approved internal image registries?
You manage a critical payment processing API. It handles 10,000 requests/minute with no tolerance for dropped connections. How do you implement zero-downtime deployments?
Your e-commerce platform releases features weekly. A bad release last quarter caused 30 minutes of downtime and lost $200K in revenue. Leadership now requires that all releases be validated against 5% of live traffic before full rollout, with automatic rollback if error rates exceed 1%. How do you implement this?
What happens under the hood during a `kubectl apply` request from client-side YAML parsing, OpenAPI validation, authentication/authorization, admission webhooks, to etcd persistence?
What is the operational difference between CPU/memory resource requests and limits, and how does Kubernetes use requests for scheduling while using limits to enforce throttling and OOM eviction?
What are the primary performance tuning levers for etcd (such as heartbeat intervals, election timeouts, disk I/O priorities, and DB quota limits) to ensure cluster stability under heavy write loads?
Prerequisite knowledge: Resource requests/limits, HPA basics, Prometheus fundamentals
Your cluster is running out of resources. Pods are stuck in Pending state, nodes are at 90% CPU, and the on-call engineer is getting paged at 2 AM. What monitoring and scaling strategies do you implement to prevent this and respond automatically?
Implement a three-layer autoscaling stack: HPA (scales pods based on metrics), VPA (right-sizes pod resource requests), and Cluster Autoscaler (adds/removes nodes). Add Prometheus alerts for early warning, and enforce LimitRanges and ResourceQuotas to prevent resource starvation.
Layer 3: Infrastructure
┌────────────────────────────────────────┐
│ Cluster Autoscaler │
│ Adds nodes when pods can't schedule │
│ Removes underutilized nodes │
└────────────────┬───────────────────────┘
│ triggers
Layer 2: Pod Scaling
┌────────────────▼───────────────────────┐
│ HPA (Horizontal Pod Autoscaler) │ ← Scale OUT (more pods)
│ VPA (Vertical Pod Autoscaler) │ ← Scale UP (bigger pods)
│ KEDA (Event-driven autoscaling) │ ← Scale on custom metrics
└────────────────┬───────────────────────┘
│ informed by
Layer 1: Observability
┌────────────────▼───────────────────────┐
│ Prometheus + Grafana │
│ Metrics Server │
│ Alert Manager │
└────────────────────────────────────────┘
Step 1: Metrics Server (prerequisite for HPA)
# Install Metrics Server (required for kubectl top and HPA)
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
# Verify it's working
kubectl top nodes
kubectl top pods -A
# Expected output:
# NAME CPU(cores) CPU% MEMORY(bytes) MEMORY%
# worker-node-01 1823m 45% 6241Mi 78%
# worker-node-02 2100m 52% 7100Mi 88% ← Getting critical
Step 2: Horizontal Pod Autoscaler (HPA v2)
# hpa-ecommerce.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: ecommerce-api-hpa
namespace: production
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: ecommerce-api
minReplicas: 5
maxReplicas: 50
metrics:
# CPU-based scaling (classic)
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70 # Scale when avg CPU > 70% of request
# Memory-based scaling
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
# Custom metric: requests per second (via Prometheus Adapter)
- type: Pods
pods:
metric:
name: http_requests_per_second
target:
type: AverageValue
averageValue: "1000" # Scale when >1000 RPS per pod
# External metric: SQS queue depth (via KEDA or custom adapter)
- type: External
external:
metric:
name: sqs_queue_depth
selector:
matchLabels:
queue: order-processing
target:
type: AverageValue
averageValue: "100" # 1 pod per 100 messages in queue
behavior:
scaleUp:
stabilizationWindowSeconds: 60 # Wait 60s before scaling up again
policies:
- type: Pods
value: 5 # Add at most 5 pods at once
periodSeconds: 60
- type: Percent
value: 100 # Or double pod count
periodSeconds: 60
selectPolicy: Max # Use whichever adds MORE pods (aggressive scale-up)
scaleDown:
stabilizationWindowSeconds: 300 # Wait 5 min before scaling down (avoid flapping)
policies:
- type: Pods
value: 2 # Remove at most 2 pods at once
periodSeconds: 120
selectPolicy: Min # Use whichever removes FEWER pods (conservative scale-down)
Step 3: Vertical Pod Autoscaler (VPA) for Right-Sizing
# Install VPA
git clone https://github.com/kubernetes/autoscaler.git
cd autoscaler/vertical-pod-autoscaler
./hack/vpa-install.sh
# vpa-ecommerce.yaml
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: ecommerce-api-vpa
namespace: production
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: ecommerce-api
updatePolicy:
updateMode: "Off" # Options: Off | Initial | Recreate | Auto
# Off → Only generates recommendations, no changes (use for analysis)
# Initial → Apply recommendations only to NEW pods
# Recreate → Evict and recreate pods with new resources (causes brief disruption)
# Auto → Same as Recreate currently (future: in-place resize)
resourcePolicy:
containerPolicies:
- containerName: ecommerce-api
minAllowed:
cpu: "100m" # Never go below 100m CPU
memory: "256Mi" # Never go below 256Mi RAM
maxAllowed:
cpu: "4" # Never exceed 4 CPU cores
memory: "4Gi" # Never exceed 4Gi RAM
controlledResources:
- cpu
- memory
# Check VPA recommendations (your right-sizing advisor)
kubectl describe vpa ecommerce-api-vpa -n production
# Output shows:
# Recommendation:
# Container Recommendations:
# Container Name: ecommerce-api
# Lower Bound:
# Cpu: 200m
# Memory: 400Mi
# Target: ← What VPA recommends you set
# Cpu: 450m
# Memory: 600Mi
# Upper Bound:
# Cpu: 1200m
# Memory: 1500Mi
⚠️ Important: Do NOT run VPA in
Automode alongside HPA CPU/memory metrics simultaneously — they conflict. Use VPA inOffmode for recommendations, apply them manually, then use HPA for scaling.
Step 4: KEDA for Event-Driven Scaling
# Install KEDA
helm repo add kedacore https://kedacore.github.io/charts
helm install keda kedacore/keda --namespace keda --create-namespace
# keda-scaledobject.yaml — Scale based on Kafka lag
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
name: order-processor-scaledobject
namespace: production
spec:
scaleTargetRef:
name: order-processor
minReplicaCount: 1
maxReplicaCount: 30
cooldownPeriod: 120 # Seconds to wait before scaling down
pollingInterval: 15 # Check trigger every 15 seconds
triggers:
# Scale based on Kafka consumer group lag
- type: kafka
metadata:
bootstrapServers: kafka.production.svc.cluster.local:9092
consumerGroup: order-processor-group
topic: orders
lagThreshold: "50" # 1 pod per 50 unprocessed messages
offsetResetPolicy: latest
# Also scale based on Prometheus metric
- type: prometheus
metadata:
serverAddress: http://prometheus.monitoring.svc.cluster.local:9090
metricName: pending_orders_count
query: sum(pending_orders_total{namespace="production"})
threshold: "100"
Step 5: Cluster Autoscaler
# cluster-autoscaler-deployment.yaml (AWS EKS example)
apiVersion: apps/v1
kind: Deployment
metadata:
name: cluster-autoscaler
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
app: cluster-autoscaler
template:
metadata:
labels:
app: cluster-autoscaler
annotations:
cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
spec:
serviceAccountName: cluster-autoscaler
containers:
- image: registry.k8s.io/autoscaling/cluster-autoscaler:v1.29.0
name: cluster-autoscaler
command:
- ./cluster-autoscaler
- --v=4
- --stderrthreshold=info
- --cloud-provider=aws
- --skip-nodes-with-local-storage=false
- --expander=least-waste # Choose node group that wastes least resources
- --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/my-cluster
- --balance-similar-node-groups # Keep node groups balanced
- --scale-down-delay-after-add=5m # Wait 5m after scale-up before scaling down
- --scale-down-unneeded-time=10m # Node must be unneeded for 10m to scale down
- --scale-down-utilization-threshold=0.5 # Scale down if node < 50% utilized
- --max-graceful-termination-sec=600
# Annotate nodes to prevent Cluster Autoscaler from removing them
kubectl annotate node worker-node-01 \
cluster-autoscaler.kubernetes.io/scale-down-disabled=true
# Check Cluster Autoscaler status and decisions
kubectl -n kube-system logs -f deployment/cluster-autoscaler | grep -E "scale|node"
Step 6: ResourceQuotas and LimitRanges (Prevent Resource Starvation)
# resource-quota-production.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
name: production-quota
namespace: production
spec:
hard:
# Compute limits
requests.cpu: "100" # Total CPU requests across all pods
requests.memory: "200Gi" # Total memory requests
limits.cpu: "200"
limits.memory: "400Gi"
# Object count limits
pods: "500"
services: "50"
persistentvolumeclaims: "100"
secrets: "200"
configmaps: "200"
# Storage limits
requests.storage: "5Ti"
---
# limit-range.yaml — Default limits for pods that don't specify
apiVersion: v1
kind: LimitRange
metadata:
name: production-limit-range
namespace: production
spec:
limits:
- type: Container
default: # Applied if container doesn't set limits
cpu: "500m"
memory: "512Mi"
defaultRequest: # Applied if container doesn't set requests
cpu: "100m"
memory: "128Mi"
max: # Hard ceiling per container
cpu: "4"
memory: "8Gi"
min: # Hard floor per container
cpu: "50m"
memory: "64Mi"
- type: Pod
max: # Hard ceiling per pod (sum of all containers)
cpu: "8"
memory: "16Gi"
- type: PersistentVolumeClaim
max:
storage: "100Gi"
min:
storage: "1Gi"
Step 7: Prometheus Alerting Rules (Early Warning System)
# prometheus-alerts.yaml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
name: cluster-resource-alerts
namespace: monitoring
spec:
groups:
- name: cluster.resources
interval: 30s
rules:
# Alert: Node CPU critical
- alert: NodeCPUCritical
expr: |
(1 - avg(rate(node_cpu_seconds_total{mode="idle"}[5m])) by (node)) * 100 > 85
for: 5m
labels:
severity: critical
team: platform
annotations:
summary: "Node {{ $labels.node }} CPU above 85%"
description: "Node CPU has been above 85% for 5 minutes. Current: {{ $value }}%"
runbook: "https://wiki.mycompany.com/runbooks/node-cpu-high"
# Alert: Node Memory critical
- alert: NodeMemoryCritical
expr: |
(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100 > 85
for: 5m
labels:
severity: critical
annotations:
summary: "Node {{ $labels.node }} memory above 85%"
# Alert: Pods stuck in Pending (early indicator of resource exhaustion)
- alert: PodsPendingTooLong
expr: |
kube_pod_status_phase{phase="Pending"} == 1
for: 10m
labels:
severity: warning
annotations:
summary: "Pod {{ $labels.namespace }}/{{ $labels.pod }} stuck Pending"
description: "Pod has been Pending for >10 minutes. Check node resources and events."
# Alert: HPA at max replicas (scaling ceiling reached)
- alert: HPAAtMaxReplicas
expr: |
kube_horizontalpodautoscaler_status_current_replicas
==
kube_horizontalpodautoscaler_spec_max_replicas
for: 15m
labels:
severity: warning
annotations:
summary: "HPA {{ $labels.namespace }}/{{ $labels.horizontalpodautoscaler }} at max replicas"
description: "HPA has been at maximum replicas for 15 minutes. Consider increasing maxReplicas or node capacity."
# Alert: Cluster CPU capacity warning (across all nodes)
- alert: ClusterCPUCapacityWarning
expr: |
sum(kube_node_status_allocatable{resource="cpu"}) -
sum(kube_pod_container_resource_requests{resource="cpu"}) < 10
for: 5m
labels:
severity: warning
annotations:
summary: "Cluster has less than 10 CPU cores of unallocated capacity"
Step 8: Grafana Dashboard Essentials
# Import community dashboards for cluster monitoring
# Kubernetes Cluster Overview: Dashboard ID 7249
# Node Exporter Full: Dashboard ID 1860
# HPA Overview: Dashboard ID 10257
# Kubernetes Capacity Planning: Dashboard ID 5309
# Key panels to build custom dashboards:
# 1. CPU/Memory allocation vs actual usage per namespace
# 2. Pending pods over time
# 3. HPA current vs max replicas
# 4. Node capacity remaining
# 5. Cluster Autoscaler activity (scale-up/down events)
| Tool | Best For | Limitation |
|---|---|---|
| HPA (CPU/Memory) | Stateless apps with predictable load | Reactive, not proactive; CPU lag |
| HPA (custom metrics) | Business-metric-driven scaling | Requires Prometheus Adapter setup |
| VPA | Right-sizing long-running services | Requires pod restart to apply |
| KEDA | Queue/event-driven workloads | Additional CRD complexity |
| Cluster Autoscaler | Dynamic node provisioning | 2–5 min lag to add nodes |
| Karpenter (AWS) | Fast, flexible node provisioning | AWS-specific (has Azure port) |
Karpenter vs Cluster Autoscaler: Karpenter (developed by AWS) provisions nodes directly without Auto Scaling Groups, giving sub-minute node provisioning and more flexible instance selection. If you're on AWS, Karpenter is generally preferred over Cluster Autoscaler for its speed and flexibility.
Cluster resource management is a feedback loop, not a one-time configuration. Observability (Prometheus alerts) gives you early warning, HPA/KEDA scale your workloads reactively, VPA keeps your requests accurate, and Cluster Autoscaler ensures the underlying infrastructure grows with you. The goal is to eliminate the 2 AM page by making scaling decisions automated, data-driven, and proactive.
How Do You Benchmark and Monitor etcd Health in Production?: Architecture & Implementation?
How do Kubernetes Namespaces provide logical cluster isolation and resource scoping, and what are the limitations of namespaces regarding security and network boundary isolation?
How does Kubernetes assign Guaranteed, Burstable, and BestEffort Quality of Service (QoS) classes to Pods, and how does the kernel OOM killer use `oom_score_adj` to evict Pods during resource scarcity?
Explain the architecture, failure modes, and operational best practices for Essential kubectl Commands: Operations and Debugging Cheat Sheet in production Kubernetes environments.
A developer accidentally committed a database password and an AWS access key directly into a ConfigMap, which was then applied to the production cluster and pushed to a public GitHub repository. You're the on-call engineer. Walk through your complete incident response plan.
How do Liveness, Readiness, and Startup probes differ in purpose and behavior, and how do incorrect probe configurations cause cascading deployment failures or unnecessary restarts?
Your company has three engineering teams: Frontend (deploys React apps), Backend (deploys APIs and workers), and Database (manages PostgreSQL and Redis). Each team has developers, leads, and CI/CD service accounts. Design a complete RBAC system where teams can only access their own namespaces, leads can approve deployments, CI/CD can deploy but not delete, and platform engineers have cluster-wide admin access with audit trails.
What are the fundamental differences between imperative `kubectl` commands and declarative `kubectl apply` manifest management, and why is declarative management required for Production Infrastructure as Code?
Why are standard Deployments unsuitable for stateful applications like database clusters, and how do StatefulSets provide stable network identities, ordinal indices, and persistent storage bindings?
Explain the architecture, failure modes, and operational best practices for DaemonSets: Running Node-Level Daemon Agents for Logging and Monitoring in production Kubernetes environments.
Explain the architecture, failure modes, and operational best practices for Dry-Run Validation: Client-Side vs. Server-Side Mutation Testing in production Kubernetes environments.
You need to ensure a PostgreSQL database Pod maintains state across cluster updates, node failures, and rolling restarts. Design a production-grade StatefulSet with proper storage, backup, high availability, and operational runbook.
Explain the architecture, failure modes, and operational best practices for Anatomy of a Kubernetes Manifest: The Four Essential Structure Fields in production Kubernetes environments.
Your payment service is experiencing cascading failures. When the downstream fraud-detection service becomes slow, payment service threads pile up waiting for responses, eventually exhausting the connection pool and causing payment service itself to fail. Implement circuit breaking, retry logic, timeout policies, and traffic mirroring using Istio to make this system resilient without changing application code.
How do Role, ClusterRole, RoleBinding, and ClusterRoleBinding resources interact to enforce least-privilege authorization across namespaces and cluster-wide resources?
You have a cluster with three node types: GPU nodes (expensive), high-memory nodes, and standard nodes. ML training jobs must run only on GPU nodes, memory-intensive analytics must prefer high-memory nodes, and web frontends must be spread across standard nodes in different AZs. Also, the GPU nodes should not accept any non-ML workloads. Implement this scheduling strategy.
What is the structural and functional difference between Labels and Annotations in Kubernetes, and why should operational metadata never be used in label selectors?
What are the key architectural differences between ServiceAccounts and User Accounts in Kubernetes, and how does token projection work for Pod service account authentication?
What are the exact phase transitions a Pod undergoes from Pending to Running or Failed, and what conditions cause a Pod to remain stuck in Pending or CrashLoopBackOff?
How does the Kubernetes API extension model work using Custom Resource Definitions (CRDs) and custom controllers, and how does API aggregation differ from CRD registration?
How do Mutating and Validating Admission Webhooks differ in execution order and purpose during an API server request, and how do you prevent webhooks from blocking cluster operations if they fail?
How do Init Containers execute sequentially before main application containers start, and how can they be used for dependency waiting, schema migrations, and secure credential setup?
Explain the architecture, failure modes, and operational best practices for Deployments vs. ReplicaSets: Declarative Scaling and Pod Template Management in production Kubernetes environments.
How does etcd store Kubernetes cluster state internally using key-value MVCC (Multi-Version Concurrency Control), and how does the API server handle bbolt database compaction and revision watch streams?
What key fields inside a Deployment specification control replica count, update strategy, and pod revision history, and how does a Deployment controller manage underlying ReplicaSets?
How do `maxSurge` and `maxUnavailable` parameters control rolling update rollouts, and how do you configure them to guarantee zero-downtime deployments under heavy traffic?
How do Kubernetes Services provide stable IP addresses and DNS endpoints for transient Pods, and how does kube-proxy update iptables/IPVS rules when Pod endpoints change?
What are the functional differences and use cases among ClusterIP, NodePort, LoadBalancer, and ExternalName service types, and how does packet routing work for each?
Explain the architecture, failure modes, and operational best practices for ConfigMaps: Decoupling Application Configuration from Container Images in production Kubernetes environments.
What are the technical differences between Secrets and ConfigMaps, why is Base64 encoding insufficient for secret security, and how should sensitive data be encrypted at rest in etcd?
How does the Kubelet enforce self-healing mechanics through container restart policies and health probes, and how does the control plane reschedule Pods when a node dies?