DevOps and Cloud Interview Questions 2025: AWS, Docker, Kubernetes, CI/CD, and Infrastructure as Code

An interviewer asks: "Our deployment takes 2 hours and fails 30% of the time. How would you fix it?" A candidate responds: "I'd set up a CI/CD pipeline." That's where most people stop. The interviewer follows up: "Which tools? What stages? How do you handle rollbacks?" Silence.

DevOps interviews test whether you can actually ship reliable software, not just use buzzwords. You need to understand containers, orchestration, cloud services, infrastructure as code, and most importantly—how these pieces fit together to create reliable deployments. Here's everything you need to know.

What DevOps Interviews Actually Test

Companies hiring DevOps engineers want to know:

Can you build reliable deployments? Manual deployments don't scale Do you understand cloud infrastructure? Modern apps run on AWS/GCP/Azure Can you debug production issues? Monitoring, logging, and troubleshooting Do you automate repetitive tasks? Scripts, IaC, and CI/CD pipelines Can you make cost-effective decisions? Cloud costs matter

Docker: Containerization Fundamentals

Basic Concepts

Question: "What is Docker and why do we use it?"

Strong answer: "Docker packages applications with their dependencies into containers—isolated, lightweight environments that run consistently across development, staging, and production. It solves the 'works on my machine' problem.

Unlike VMs which virtualize hardware, containers share the host OS kernel, making them much lighter:

• VM: Includes full OS (GBs), slow to start (minutes)
• Container: Just app + dependencies (MBs), starts in seconds

Benefits:

• Consistency across environments
• Easy scaling (spin up/down containers quickly)
• Isolation (dependencies don't conflict)
• Efficient resource usage (vs VMs)"

Dockerfile Best Practices

Question: "Write an optimized Dockerfile for a Node.js application."

Bad example:

FROM node:18
COPY . /app
WORKDIR /app
RUN npm install
CMD ["node", "server.js"]

Problems:

• No layer caching optimization
• Installs dev dependencies
• Runs as root user (security risk)
• Large base image

Good example:

# Use Alpine Linux for smaller image (18-alpine vs 18 = 100MB vs 900MB)
FROM node:18-alpine AS builder

# Set working directory
WORKDIR /app

# Copy package files first (layer caching)
# Only re-runs npm install if package.json changes
COPY package*.json ./

# Install dependencies
RUN npm ci --only=production

# Copy application code
COPY . .

# Multi-stage build - final image doesn't include build tools
FROM node:18-alpine

WORKDIR /app

# Create non-root user
RUN addgroup -g 1001 -S nodejs && \
    adduser -S nodejs -u 1001

# Copy built app from builder stage
COPY --from=builder --chown=nodejs:nodejs /app .

# Switch to non-root user
USER nodejs

# Expose port
EXPOSE 3000

# Health check
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s \
  CMD node healthcheck.js || exit 1

# Start application
CMD ["node", "server.js"]

Key optimizations:

• Multi-stage build reduces final image size
• Layer caching (package.json copied separately)
• Non-root user (security)
• Alpine base image (smaller size)
• Health check for container orchestration
• npm ci instead of npm install (faster, more reliable)

Docker Commands

Question: "Explain essential Docker commands and when to use them."

# Build image
docker build -t myapp:v1.0 .
docker build -t myapp:latest --no-cache .  # Force rebuild

# Run container
docker run -d \                    # Detached mode (background)
  -p 3000:3000 \                  # Port mapping (host:container)
  --name myapp \                  # Container name
  --env-file .env \               # Environment variables
  --restart unless-stopped \      # Auto-restart policy
  -v $(pwd)/data:/app/data \      # Volume mount
  myapp:latest

# List containers
docker ps                         # Running containers
docker ps -a                      # All containers (including stopped)

# View logs
docker logs myapp                 # View logs
docker logs -f myapp              # Follow logs (like tail -f)
docker logs --tail 100 myapp      # Last 100 lines

# Execute command in container
docker exec -it myapp bash        # Interactive shell
docker exec myapp ls /app         # Run command

# Stop/remove
docker stop myapp                 # Graceful stop
docker kill myapp                 # Force stop
docker rm myapp                   # Remove container
docker rmi myapp:latest          # Remove image

# System cleanup
docker system prune -a            # Remove unused containers/images/networks
docker volume prune               # Remove unused volumes

# Inspect container
docker inspect myapp              # Full container details
docker stats myapp                # Resource usage (CPU, memory, network)

Docker Compose for Multi-Container Apps

Question: "Set up a web app with database and cache using Docker Compose."

# docker-compose.yml
version: '3.8'

services:
  # Application server
  app:
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - "3000:3000"
    environment:
      NODE_ENV: production
      DATABASE_URL: postgres://user:password@db:5432/myapp
      REDIS_URL: redis://cache:6379
    depends_on:
      - db
      - cache
    volumes:
      - ./uploads:/app/uploads
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  # PostgreSQL database
  db:
    image: postgres:15-alpine
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password
      POSTGRES_DB: myapp
    volumes:
      - postgres-data:/var/lib/postgresql/data
      - ./init.sql:/docker-entrypoint-initdb.d/init.sql
    ports:
      - "5432:5432"
    restart: unless-stopped

  # Redis cache
  cache:
    image: redis:7-alpine
    command: redis-server --appendonly yes
    volumes:
      - redis-data:/data
    restart: unless-stopped

  # Nginx reverse proxy
  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
      - ./ssl:/etc/nginx/ssl:ro
    depends_on:
      - app
    restart: unless-stopped

volumes:
  postgres-data:
  redis-data:

networks:
  default:
    driver: bridge

Commands:

docker-compose up -d              # Start all services
docker-compose down               # Stop and remove all services
docker-compose logs -f app        # Follow logs for app service
docker-compose exec app sh        # Shell into app container
docker-compose restart app        # Restart specific service
docker-compose ps                 # List services

Kubernetes: Container Orchestration

Core Concepts

Question: "Explain Kubernetes architecture and key components."

"Kubernetes orchestrates containers across multiple servers (nodes). Key components:

Control Plane (Master):

• API Server: Frontend for Kubernetes. All commands go through it
• etcd: Key-value store for cluster state
• Scheduler: Assigns pods to nodes based on resources
• Controller Manager: Maintains desired state (replicas, endpoints, etc.)

Worker Nodes:

• Kubelet: Agent that runs containers and reports status
• Container Runtime: Docker/containerd that actually runs containers
• Kube-proxy: Network proxy for service communication

Key Resources:

• Pod: Smallest unit, contains 1+ containers
• Deployment: Manages replica sets and rolling updates
• Service: Stable network endpoint for pods
• ConfigMap/Secret: Configuration and sensitive data
• Ingress: HTTP/HTTPS routing
• PersistentVolume: Storage that outlives pods"

Kubernetes Manifests

Question: "Deploy a web application with database on Kubernetes."

Deployment (app):

# app-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  labels:
    app: myapp
spec:
  replicas: 3                    # Run 3 pods for high availability
  selector:
    matchLabels:
      app: myapp
  strategy:
    type: RollingUpdate          # Zero-downtime updates
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: myregistry/myapp:v1.0
        ports:
        - containerPort: 3000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: app-secrets
              key: database-url
        - name: NODE_ENV
          value: "production"
        resources:
          requests:               # Minimum guaranteed
            memory: "256Mi"
            cpu: "250m"
          limits:                 # Maximum allowed
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:            # Restart if fails
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:           # Remove from load balancer if fails
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5

Service (load balancer):

# app-service.yaml
apiVersion: v1
kind: Service
metadata:
  name: myapp-service
spec:
  type: LoadBalancer            # Creates external load balancer
  selector:
    app: myapp                  # Routes to pods with this label
  ports:
  - protocol: TCP
    port: 80                    # External port
    targetPort: 3000            # Container port

ConfigMap (configuration):

# app-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: app-config
data:
  app.conf: |
    log_level=info
    max_connections=100
  feature_flags.json: |
    {
      "new_feature": true,
      "beta_mode": false
    }

Secret (sensitive data):

# app-secret.yaml
apiVersion: v1
kind: Secret
metadata:
  name: app-secrets
type: Opaque
data:
  database-url: cG9zdGdyZXM6Ly8uLi4=  # base64 encoded
  api-key: c2VjcmV0a2V5MTIz              # base64 encoded

Ingress (HTTP routing):

# app-ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: myapp-ingress
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
spec:
  tls:
  - hosts:
    - myapp.com
    secretName: myapp-tls
  rules:
  - host: myapp.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: myapp-service
            port:
              number: 80

Commands:

# Apply manifests
kubectl apply -f app-deployment.yaml
kubectl apply -f app-service.yaml

# Get resources
kubectl get pods
kubectl get deployments
kubectl get services

# Describe (detailed info)
kubectl describe pod myapp-xxx

# Logs
kubectl logs myapp-xxx
kubectl logs -f myapp-xxx --tail=100

# Execute command in pod
kubectl exec -it myapp-xxx -- /bin/sh

# Scale deployment
kubectl scale deployment myapp --replicas=5

# Update image (rolling update)
kubectl set image deployment/myapp myapp=myregistry/myapp:v2.0

# Rollback
kubectl rollout undo deployment/myapp

# Delete resources
kubectl delete -f app-deployment.yaml
kubectl delete pod myapp-xxx

AWS: Cloud Services

Core AWS Services

Question: "Design a highly available web application on AWS."

Architecture:

Internet
   │
   ├──────────────────────────────────┐
   │                                  │
Route 53 (DNS)                    CloudFront (CDN)
   │                                  │
   │                              S3 (Static Assets)
   │
Application Load Balancer
   │
   ├──────────────┬──────────────┐
   │              │              │
 EC2 (AZ-1)   EC2 (AZ-2)    EC2 (AZ-3)
   │              │              │
   └──────────────┴──────────────┘
                  │
         ┌────────┴────────┐
         │                 │
    RDS Primary      ElastiCache
   (Multi-AZ)         (Redis)
         │
    RDS Standby

Key services explained:

Compute:

• EC2: Virtual servers, full control
• Lambda: Serverless functions, pay per execution
• ECS/EKS: Container orchestration (Docker/Kubernetes)
• Elastic Beanstalk: PaaS, deploys apps automatically

Storage:

• S3: Object storage (images, videos, backups)
• EBS: Block storage for EC2 (like hard drives)
• EFS: Shared file system across EC2 instances
• Glacier: Archive storage (cheap, slow retrieval)

Database:

• RDS: Managed relational databases (PostgreSQL, MySQL)
• DynamoDB: NoSQL, serverless, auto-scaling
• ElastiCache: Managed Redis/Memcached
• Redshift: Data warehousing (analytics)

Networking:

• VPC: Virtual private network, isolate resources
• Route 53: DNS service
• CloudFront: CDN, caches content globally
• API Gateway: Create/manage REST APIs

Monitoring/Logging:

• CloudWatch: Metrics, logs, alarms
• CloudTrail: Audit logs (who did what)
• X-Ray: Distributed tracing

EC2 and Auto Scaling

Question: "Set up auto-scaling for a web application."

Auto Scaling Group configuration:

# Create launch template
aws ec2 create-launch-template \
  --launch-template-name web-app-template \
  --version-description "v1" \
  --launch-template-data '{
    "ImageId": "ami-0abcdef1234567890",
    "InstanceType": "t3.medium",
    "KeyName": "my-key-pair",
    "SecurityGroupIds": ["sg-0abc123"],
    "UserData": "base64-encoded-startup-script",
    "IamInstanceProfile": {
      "Name": "EC2-S3-Access-Role"
    }
  }'

# Create Auto Scaling Group
aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name web-app-asg \
  --launch-template "LaunchTemplateName=web-app-template,Version=1" \
  --min-size 2 \
  --max-size 10 \
  --desired-capacity 3 \
  --target-group-arns arn:aws:elasticloadbalancing:... \
  --vpc-zone-identifier "subnet-abc,subnet-def,subnet-ghi" \
  --health-check-type ELB \
  --health-check-grace-period 300

# Create scaling policies
# Scale up when CPU > 70%
aws autoscaling put-scaling-policy \
  --auto-scaling-group-name web-app-asg \
  --policy-name scale-up \
  --scaling-adjustment 2 \
  --adjustment-type ChangeInCapacity \
  --cooldown 300

# CloudWatch alarm to trigger scale-up
aws cloudwatch put-metric-alarm \
  --alarm-name cpu-high \
  --alarm-description "Scale up when CPU exceeds 70%" \
  --metric-name CPUUtilization \
  --namespace AWS/EC2 \
  --statistic Average \
  --period 300 \
  --threshold 70 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 2 \
  --alarm-actions arn:aws:autoscaling:...

IAM (Identity and Access Management)

Question: "Explain IAM policies and best practices."

IAM Policy (JSON):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:ListBucket",
      "Resource": "arn:aws:s3:::my-bucket"
    },
    {
      "Effect": "Deny",
      "Action": "s3:DeleteBucket",
      "Resource": "*"
    }
  ]
}

Best practices:

1. Principle of least privilege: Only grant permissions needed
2. Use roles for EC2/Lambda: Don't embed credentials in code
3. Enable MFA for privileged users
4. Rotate access keys regularly
5. Use policy conditions for additional security:

{
  "Effect": "Allow",
  "Action": "s3:*",
  "Resource": "*",
  "Condition": {
    "IpAddress": {
      "aws:SourceIp": "203.0.113.0/24"  // Only from office IP
    },
    "DateGreaterThan": {
      "aws:CurrentTime": "2025-01-01T00:00:00Z"
    }
  }
}

CI/CD Pipelines

Question: "Design a CI/CD pipeline for a Node.js application."

GitHub Actions example:

# .github/workflows/deploy.yml
name: CI/CD Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

env:
  AWS_REGION: us-east-1
  ECR_REPOSITORY: myapp
  ECS_SERVICE: myapp-service
  ECS_CLUSTER: production

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'
          cache: 'npm'

      - name: Install dependencies
        run: npm ci

      - name: Run linter
        run: npm run lint

      - name: Run tests
        run: npm test

      - name: Check code coverage
        run: npm run coverage
        env:
          COVERAGE_THRESHOLD: 80

  build:
    needs: test
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v2
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: ${{ env.AWS_REGION }}

      - name: Login to Amazon ECR
        id: login-ecr
        uses: aws-actions/amazon-ecr-login@v1

      - name: Build, tag, and push image
        env:
          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
          IMAGE_TAG: ${{ github.sha }}
        run: |
          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
          docker tag $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG \
                     $ECR_REGISTRY/$ECR_REPOSITORY:latest
          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
          docker push $ECR_REGISTRY/$ECR_REPOSITORY:latest

  deploy:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v2
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: ${{ env.AWS_REGION }}

      - name: Deploy to ECS
        run: |
          aws ecs update-service \
            --cluster ${{ env.ECS_CLUSTER }} \
            --service ${{ env.ECS_SERVICE }} \
            --force-new-deployment

      - name: Wait for deployment
        run: |
          aws ecs wait services-stable \
            --cluster ${{ env.ECS_CLUSTER }} \
            --services ${{ env.ECS_SERVICE }}

      - name: Notify Slack
        uses: 8398a7/action-slack@v3
        with:
          status: ${{ job.status }}
          text: 'Deployment to production completed!'
          webhook_url: ${{ secrets.SLACK_WEBHOOK }}
        if: always()

Pipeline stages explained:

1. Test: Run linter, unit tests, integration tests
2. Build: Create Docker image, push to registry
3. Deploy: Update ECS service, wait for stability
4. Notify: Alert team via Slack/email

Infrastructure as Code (Terraform)

Question: "Use Terraform to provision AWS infrastructure."

# main.tf
terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
  backend "s3" {
    bucket = "mycompany-terraform-state"
    key    = "production/terraform.tfstate"
    region = "us-east-1"
  }
}

provider "aws" {
  region = var.aws_region
}

# VPC
resource "aws_vpc" "main" {
  cidr_block           = "10.0.0.0/16"
  enable_dns_hostnames = true
  enable_dns_support   = true

  tags = {
    Name        = "production-vpc"
    Environment = "production"
  }
}

# Subnets
resource "aws_subnet" "public" {
  count                   = 3
  vpc_id                  = aws_vpc.main.id
  cidr_block              = "10.0.${count.index}.0/24"
  availability_zone       = data.aws_availability_zones.available.names[count.index]
  map_public_ip_on_launch = true

  tags = {
    Name = "public-subnet-${count.index + 1}"
  }
}

# Security Group
resource "aws_security_group" "web" {
  name        = "web-sg"
  description = "Security group for web servers"
  vpc_id      = aws_vpc.main.id

  ingress {
    from_port   = 80
    to_port     = 80
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  ingress {
    from_port   = 443
    to_port     = 443
    protocol    = "tcp"
    cidr_blocks = ["0.0.0.0/0"]
  }

  egress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }
}

# RDS Database
resource "aws_db_instance" "postgres" {
  identifier             = "myapp-db"
  engine                 = "postgres"
  engine_version         = "15.3"
  instance_class         = "db.t3.medium"
  allocated_storage      = 100
  storage_type           = "gp3"
  db_name                = "myapp"
  username               = var.db_username
  password               = var.db_password
  multi_az               = true
  backup_retention_period = 7
  skip_final_snapshot    = false
  final_snapshot_identifier = "myapp-final-snapshot"

  vpc_security_group_ids = [aws_security_group.db.id]
  db_subnet_group_name   = aws_db_subnet_group.main.name

  tags = {
    Environment = "production"
  }
}

# Outputs
output "db_endpoint" {
  value       = aws_db_instance.postgres.endpoint
  description = "Database endpoint"
  sensitive   = true
}

Commands:

terraform init              # Initialize Terraform
terraform plan              # Preview changes
terraform apply             # Apply changes
terraform destroy           # Destroy infrastructure
terraform fmt               # Format code
terraform validate          # Validate configuration
terraform show              # Show current state
terraform output db_endpoint # Show specific output

Monitoring and Logging

Question: "Set up monitoring for production application."

CloudWatch Metrics + Alarms:

# Create custom metric
aws cloudwatch put-metric-data \
  --namespace MyApp \
  --metric-name OrdersPerMinute \
  --value 42 \
  --timestamp $(date -u +"%Y-%m-%dT%H:%M:%SZ")

# Create alarm
aws cloudwatch put-metric-alarm \
  --alarm-name high-error-rate \
  --alarm-description "Alert when error rate exceeds 5%" \
  --metric-name ErrorRate \
  --namespace MyApp \
  --statistic Average \
  --period 300 \
  --threshold 5 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 2 \
  --alarm-actions arn:aws:sns:us-east-1:123456789:alerts

Prometheus + Grafana (Kubernetes):

# prometheus-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod
        relabel_configs:
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
            action: keep
            regex: true
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
            action: replace
            target_label: __metrics_path__
            regex: (.+)

ELK Stack for logging:

# filebeat-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: filebeat-config
data:
  filebeat.yml: |
    filebeat.inputs:
    - type: container
      paths:
        - /var/log/containers/*.log
      processors:
        - add_kubernetes_metadata:
            host: ${NODE_NAME}
            matchers:
            - logs_path:
                logs_path: "/var/log/containers/"

    output.elasticsearch:
      hosts: ['${ELASTICSEARCH_HOST}:${ELASTICSEARCH_PORT}']
      username: ${ELASTICSEARCH_USERNAME}
      password: ${ELASTICSEARCH_PASSWORD}

Common Interview Questions

"Explain blue-green deployment vs canary deployment."

Blue-Green:

Blue environment (current): v1.0 (100% traffic)
Green environment (new): v2.0 (0% traffic)

1. Deploy v2.0 to Green
2. Test Green environment
3. Switch traffic: Blue 0% → Green 100%
4. Keep Blue as backup for quick rollback

Canary:

Current: v1.0 (95% traffic)
Canary: v2.0 (5% traffic)

1. Deploy v2.0 to small subset
2. Monitor metrics (errors, latency)
3. Gradually increase: 5% → 25% → 50% → 100%
4. Rollback if issues detected

"How do you handle secrets in production?"

"Never commit secrets to Git. Use:

• AWS Secrets Manager/Parameter Store: Encrypted, rotatable, audit logs
• HashiCorp Vault: Centralized secret management
• Kubernetes Secrets: Base64 encoded, encrypted at rest
• Environment variables: From secret stores, not hardcoded

Example with AWS Secrets Manager:

# Store secret
aws secretsmanager create-secret \
  --name prod/database/password \
  --secret-string "super-secure-password"

# Retrieve in application
aws secretsmanager get-secret-value \
  --secret-id prod/database/password \
  --query SecretString --output text
```"

## How to Prepare

1. **Get hands-on experience:** Set up a personal project with Docker, Kubernetes, and CI/CD
2. **Use free tiers:** AWS, GCP, Azure all have free tiers for learning
3. **Practice on [Vibe Interviews](https://vibeinterviews.com):** Get DevOps questions with instant feedback
4. **Learn IaC:** Write Terraform for AWS resources
5. **Study production architectures:** Read engineering blogs from Netflix, Airbnb, Uber
6. **Understand monitoring:** Set up Prometheus/Grafana or CloudWatch
7. **Practice troubleshooting:** Intentionally break things and fix them

DevOps interviews test your ability to ship reliable, scalable systems. Master containerization, orchestration, cloud services, and automation—you'll stand out whether you're interviewing for DevOps, SRE, or platform engineering roles.