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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?
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?
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?
Prerequisite knowledge: Nodes, Labels, Resource requests/limits, basic scheduling concepts
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.
Use taints on GPU nodes to repel non-ML workloads (with NoSchedule), tolerations on ML jobs to accept the GPU taint, nodeAffinity to target specific node types, podAntiAffinity with topologyKey: topology.kubernetes.io/zone to spread frontends across AZs, and topology spread constraints for fine-grained distribution control.
Cluster Nodes:
┌─────────────────────────────────────────────────────────────┐
│ GPU Nodes (4x) High-Memory Nodes (6x) │
│ label: node-type=gpu label: node-type=high-memory │
│ taint: gpu=true:NoSchedule taint: (none) │
│ GPU: 8x A100 RAM: 512Gi │
│ AZ: us-east-1a/1b AZ: us-east-1a/1b/1c │
│ │
│ Standard Nodes (20x) │
│ label: node-type=standard │
│ taint: (none) │
│ AZ: us-east-1a/1b/1c (balanced) │
└─────────────────────────────────────────────────────────────┘
Step 1: Label and Taint Nodes
# Label GPU nodes
for node in gpu-node-01 gpu-node-02 gpu-node-03 gpu-node-04; do
kubectl label node $node \
node-type=gpu \
accelerator=nvidia-a100 \
topology.kubernetes.io/zone=us-east-1a
# Taint: Only ML workloads with toleration can schedule here
kubectl taint node $node \
gpu=true:NoSchedule # NoSchedule: new pods rejected without toleration
# NoExecute: existing pods also evicted
# PreferNoSchedule: soft version of NoSchedule
done
# Label high-memory nodes
for node in highmem-node-01 highmem-node-02 highmem-node-03; do
kubectl label node $node \
node-type=high-memory \
memory-tier=xlarge
done
# Label standard nodes with AZ distribution
kubectl label node standard-node-01 node-type=standard topology.kubernetes.io/zone=us-east-1a
kubectl label node standard-node-02 node-type=standard topology.kubernetes.io/zone=us-east-1a
kubectl label node standard-node-03 node-type=standard topology.kubernetes.io/zone=us-east-1b
kubectl label node standard-node-04 node-type=standard topology.kubernetes.io/zone=us-east-1b
kubectl label node standard-node-05 node-type=standard topology.kubernetes.io/zone=us-east-1c
kubectl label node standard-node-06 node-type=standard topology.kubernetes.io/zone=us-east-1c
# ... continue for all 20 standard nodes
# Verify node labels and taints
kubectl get nodes -o custom-columns=\
"NAME:.metadata.name,\
TYPE:.metadata.labels.node-type,\
ZONE:.metadata.labels.topology\.kubernetes\.io/zone,\
TAINTS:.spec.taints[*].key"
Step 2: ML Training Job — GPU Nodes Only
# ml-training-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
name: ml-training-resnet50
namespace: ml-platform
spec:
parallelism: 4 # Run on all 4 GPU nodes simultaneously
completions: 4
template:
spec:
# REQUIRED: Tolerate the GPU taint to schedule on GPU nodes
tolerations:
- key: "gpu"
operator: "Equal"
value: "true"
effect: "NoSchedule"
# REQUIRED: Target GPU nodes specifically
affinity:
nodeAffinity:
# Hard requirement: MUST be on GPU node
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node-type
operator: In
values: ["gpu"]
- key: accelerator
operator: In
values: ["nvidia-a100"] # Specific GPU model required
# Soft preference: prefer nodes with fewer running jobs
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 50
preference:
matchExpressions:
- key: workload-count
operator: Lt
values: ["2"]
# Spread across different GPU nodes (one job per node)
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchLabels:
job-name: ml-training-resnet50
topologyKey: kubernetes.io/hostname # One pod per node
containers:
- name: training
image: registry.mycompany.com/ml-trainer:latest
command: ["python", "train.py", "--model=resnet50", "--epochs=100"]
# Request GPU resources
resources:
requests:
cpu: "8"
memory: "64Gi"
nvidia.com/gpu: "1" # Request 1 GPU per pod
limits:
cpu: "16"
memory: "128Gi"
nvidia.com/gpu: "1" # GPU limits must equal requests
env:
- name: CUDA_VISIBLE_DEVICES
value: "0"
- name: NCCL_DEBUG
value: "INFO"
restartPolicy: OnFailure
# Give ML jobs priority over regular workloads
priorityClassName: ml-high-priority
Step 3: Analytics — Prefer High-Memory Nodes
# analytics-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: analytics-engine
namespace: analytics
spec:
replicas: 3
selector:
matchLabels:
app: analytics-engine
template:
metadata:
labels:
app: analytics-engine
spec:
affinity:
nodeAffinity:
# Soft preference: prefer high-memory nodes
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 100 # Highest possible weight — strongly prefer this
preference:
matchExpressions:
- key: node-type
operator: In
values: ["high-memory"]
- weight: 50 # Fallback: standard nodes are acceptable
preference:
matchExpressions:
- key: node-type
operator: In
values: ["standard"]
# Hard requirement: NEVER schedule on GPU nodes
# (GPU nodes are expensive and reserved for ML)
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node-type
operator: NotIn
values: ["gpu"] # Exclude GPU nodes
# Spread analytics pods across nodes
podAntiAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 100
podAffinityTerm:
labelSelector:
matchLabels:
app: analytics-engine
topologyKey: kubernetes.io/hostname
containers:
- name: analytics
image: registry.mycompany.com/analytics:latest
resources:
requests:
cpu: "4"
memory: "128Gi" # High memory workload
limits:
cpu: "8"
memory: "256Gi"
Step 4: Frontend — Spread Across AZs with Topology Spread
# frontend-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: web-frontend
namespace: production
spec:
replicas: 12 # 4 per AZ (3 AZs × 4 pods)
selector:
matchLabels:
app: web-frontend
template:
metadata:
labels:
app: web-frontend
spec:
# Topology Spread Constraints (Kubernetes 1.19+)
# More expressive than podAntiAffinity for spreading
topologySpreadConstraints:
# Constraint 1: Spread evenly across AZs
- maxSkew: 1 # Max difference in pod count between AZs
topologyKey: topology.kubernetes.io/zone
whenUnsatisfiable: DoNotSchedule # Hard constraint
labelSelector:
matchLabels:
app: web-frontend
# With 12 replicas and 3 AZs: 4 pods per AZ
# maxSkew: 1 means AZs can have 4 or 5 pods, but not 3 and 6
# Constraint 2: Spread across nodes within each AZ
- maxSkew: 1
topologyKey: kubernetes.io/hostname
whenUnsatisfiable: ScheduleAnyway # Soft constraint
labelSelector:
matchLabels:
app: web-frontend
affinity:
# Hard requirement: only standard nodes for frontend
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node-type
operator: In
values: ["standard"]
containers:
- name: frontend
image: registry.mycompany.com/frontend:latest
resources:
requests:
cpu: "500m"
memory: "512Mi"
limits:
cpu: "1000m"
memory: "1Gi"
Step 5: Priority Classes (Ensuring ML Jobs Preempt Lower Priority)
# priority-classes.yaml
# Highest priority: ML training (preempts everything else if needed)
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: ml-high-priority
value: 1000000
globalDefault: false
preemptionPolicy: PreemptLowerPriority
description: "ML training jobs — highest priority, can preempt other workloads"
---
# High priority: Production workloads
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: production-high
value: 100000
globalDefault: false
preemptionPolicy: PreemptLowerPriority
description: "Production web services"
---
# Default priority: Standard workloads
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: default-priority
value: 1000
globalDefault: true
description: "Default priority for all workloads"
---
# Low priority: Batch jobs and background tasks
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: batch-low
value: 100
globalDefault: false
preemptionPolicy: Never # Cannot preempt others
description: "Batch jobs — lowest priority, never preempt"
Step 6: Descheduler — Continuously Rebalance Pods
# descheduler-policy.yaml
# The descheduler runs periodically and evicts pods that violate
# scheduling constraints (e.g., due to cluster changes after initial scheduling)
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
# Remove pods that violated their node affinity after node label changes
RemovePodsViolatingNodeAffinity:
enabled: true
params:
nodeAffinityType:
- requiredDuringSchedulingIgnoredDuringExecution
# Rebalance pods that are violating topology spread constraints
RemovePodsViolatingTopologySpreadConstraint:
enabled: true
params:
constraints:
- DoNotSchedule
# Remove duplicate pods on the same node (fix anti-affinity violations)
RemoveDuplicates:
enabled: true
# Evict pods from over-utilized nodes
LowNodeUtilization:
enabled: true
params:
nodeResourceUtilizationThresholds:
thresholds:
cpu: 20 # Node is underutilized if CPU < 20%
memory: 20
pods: 20
targetThresholds:
cpu: 50 # Target utilization after descheduling
memory: 50
pods: 50
# Deploy descheduler as a CronJob
helm repo add descheduler https://kubernetes-sigs.github.io/descheduler/
helm install descheduler descheduler/descheduler \
--namespace kube-system \
--set schedule="*/30 * * * *" # Run every 30 minutes
Step 7: Verify Scheduling Decisions
# Check where pods are scheduled
kubectl get pods -n ml-platform -o wide
# Verify all ml-training pods are on GPU nodes
kubectl get pods -n production -o wide | grep web-frontend
# Verify pods are spread across AZs
# Check why a pod is not scheduling (Pending state)
kubectl describe pod <pending-pod> | grep -A 20 "Events:"
# Common messages:
# "0/26 nodes are available: 4 node(s) had taint {gpu: true}..."
# "0/26 nodes are available: 6 node(s) didn't match node affinity..."
# "0/26 nodes are available: topology.kubernetes.io/zone, maxSkew=1..."
# Simulate scheduling without actually creating pods
kubectl apply --dry-run=server -f ml-training-job.yaml
# Use kube-scheduler simulator for complex scenarios
# https://github.com/kubernetes-sigs/kube-scheduler-simulator
| Mechanism | Type | Use Case | Flexibility |
|---|---|---|---|
| nodeSelector | Hard | Simple label matching | Low |
| nodeAffinity | Hard/Soft | Complex label expressions | Medium |
| podAffinity | Hard/Soft | Co-locate pods | Medium |
| podAntiAffinity | Hard/Soft | Spread pods apart | Medium |
| Taints + Tolerations | Hard/Soft | Reserve nodes for workloads | High |
| Topology Spread | Hard/Soft | Even distribution across zones | High |
| Priority + Preemption | Hard | Resource contention resolution | High |
| Descheduler | Ongoing | Post-scheduling rebalancing | High |
requiredDuringScheduling rules are checked after scheduling too." — The IgnoredDuringExecution suffix means node label changes after scheduling are ignored. Pods won't be evicted if node labels change. Use the descheduler to fix this.NoSchedule prevents NEW pods without a toleration. Add NoExecute to also evict existing pods that don't have the toleration.maxSkew). PodAntiAffinity is better for absolute co-location rules.preferredDuringScheduling is always respected." — It is a best-effort preference. Under resource pressure, the scheduler may ignore preferences entirely to schedule the pod. Don't rely on soft rules for security or compliance.Advanced scheduling is a layered system: taints/tolerations control which nodes accept which workloads (the "reservation" layer), node affinity controls which nodes pods prefer or require (the "targeting" layer), pod affinity/anti-affinity controls pod-to-pod relationships (the "social" layer), and topology spread constraints ensure even distribution (the "balance" layer). Use requiredDuringScheduling for correctness requirements (security, compliance, hardware) and preferredDuringScheduling for optimization (cost, latency).
NoSchedule, NoExecute, and PreferNoSchedule taints?IgnoredDuringExecution means in affinity rules?preferredDuringScheduling rules can be ignored?How to use this guide: Each question is tagged, timed, and leveled. Use the Index to jump to a topic, or follow the progressive sequence from Q1 → Q60. Checkboxes let you track mastery. Three answer depths are provided per question: Quick, Detailed, and Deep Dive.
| # | Question Summary | Level | Topic Tags | Time |
|---|---|---|---|---|
| Q1 | What is the Kubernetes API extension model? | Advanced | API, Extensions | 8 min |
| Q2 | How do Validating vs. Mutating Admission Webhooks differ? | Advanced | Admission, Security | 10 min |
| Q3 | How do you build a custom admission webhook from scratch? | Expert | Admission, API | 20 min |
| Q4 | What is OPA/Gatekeeper and how does it extend admission control? | Expert | Security, Policy | 15 min |
| Q5 | Scenario: Enforce image registry policy cluster-wide | Expert | Admission, Security | 15 min |
| Q6 | How does etcd store Kubernetes state internally? | Advanced | etcd, Storage | 8 min |
| Q7 | What are the key levers for etcd performance tuning? | Expert | etcd, Performance | 15 min |
| Q8 | How do you benchmark and monitor etcd health in production? | Expert | etcd, Monitoring | 12 min |
| Q9 | Scenario: etcd is slow — diagnose and fix it | Expert | etcd, Troubleshooting | 20 min |
| Q10 | How do you design etcd topology for HA clusters? | Expert | etcd, HA | 15 min |
| Q11 | What is Pod Security Admission (PSA) and how does it replace PSP? | Advanced | Security, Policy | 10 min |
| Q12 | How do you implement Seccomp and AppArmor profiles in Kubernetes? | Expert | Security, Linux | 15 min |
| Q13 | How do you harden the Kubernetes API server? | Expert | Security, API | 15 min |
| Q14 | How do you implement mTLS between microservices in Kubernetes? | Expert | Security, Networking | 15 min |
| Q15 | Scenario: A developer leaked credentials in a ConfigMap | Advanced | Security, Secrets | 12 min |
| Q16 | Scenario: Design a secure RBAC setup for 3 teams | Advanced | Security, RBAC | 15 min |
| Q17 | How do you scale Kubernetes control plane components? | Expert | Scaling, HA | 15 min |
| Q18 | How does the Horizontal Pod Autoscaler work internally? | Advanced | Scaling, HPA | 10 min |
| Q19 | How do you implement custom metrics for HPA scaling? | Expert | Scaling, Metrics | 15 min |
| Q20 | What is KEDA and when should you use it over native HPA? | Expert | Scaling, KEDA | 12 min |
| Q21 | Scenario: Cluster is running out of resources — what do you do? | Advanced | Scaling, Monitoring | 15 min |
| Q22 | How do you manage and monitor 100+ clusters at scale? | Expert | Multi-cluster, Monitoring | 20 min |
| Q23 | How do you tune kube-scheduler for large clusters? | Expert | Scheduling, Performance | 15 min |
| Q24 | How do you optimize container startup time (cold start)? | Advanced | Performance, Pods | 10 min |
| Q25 | How do you configure resource QoS classes and why does it matter? | Advanced | Performance, Resources | 10 min |
| Q26 | How do CNI plugin choices affect network performance? | Expert | Networking, Performance | 15 min |
| Q27 | Scenario: Stateless web app that scales on traffic | Advanced | Deployments, Scaling | 15 min |
| Q28 | How do you implement zero-downtime deployments? | Advanced | Deployments, HA | 12 min |
| Q29 | Scenario: Pod is stuck in Pending state | Intermediate | Troubleshooting, Scheduling | 10 min |
| Q30 | Scenario: Stateful database across cluster updates | Advanced | Storage, StatefulSets | 15 min |
| Q31 | How do you implement Velero for cluster backup and DR? | Expert | DR, Storage | 15 min |
| Q32 | How do you design a multi-cluster disaster recovery strategy? | Expert | DR, Multi-cluster | 20 min |
| Q33 | Scenario: Design a geographically distributed multi-cluster setup | Expert | Multi-cluster, DR | 20 min |
| Q34 | How do you manage microservice dependencies in Kubernetes? | Advanced | Microservices, Networking | 12 min |
| Q35 | How does a service mesh (Istio/Linkerd) manage traffic? | Expert | Networking, Service Mesh | 15 min |
| Q36 | How do you implement circuit breaking and retry logic? | Expert | Microservices, Resilience | 12 min |
| Q37 | What are advanced Helm chart patterns (library charts, hooks)? | Advanced | Helm, Packaging | 12 min |
| Q38 | How do you develop a custom Helm plugin? | Expert | Helm, Plugins | 20 min |
| Q39 | How do you manage multi-environment Helm releases at scale? | Expert | Helm, GitOps | 15 min |
| Q40 | Scenario: Build a Helm plugin that enforces chart standards | Expert | Helm, Policy | 20 min |
This section assumes you know: CRDs, basic RBAC, TLS concepts, basic Go or Python.
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?