• Experience: 6 years as a DevOps Engineer
• Project: AI-powered mobile application for centralized smart home accessory control
• Tech Stack:
  • DevOps Tools: Docker, Kubernetes, Jenkins, Git, Ansible, Terraform, Helm, Istio, GitHub Actions, GitLab, ELK, Prometheus, Grafana, Spinnaker, ArgoCD, Apache2, Nginx, Tomcat 8, GitLab, Datadog, Rio, Nomad
  • Programming Languages: Python, Groovy, Shell Scripting, YAML
  • Operating Systems: Ubuntu, RedHat
  • Cloud Services: AWS, GCP

1. Building and maintaining the CI/CD pipeline using Jenkins and later GitHub Actions, reducing deployment time by 70%.
2. Managing Kubernetes clusters on AWS EKS and GCP GKE, handling the infrastructure for 300+ microservices that powered the AI backend.
3. Implementing infrastructure as code using Terraform to provision and manage all cloud resources across AWS and GCP environments.
4. Setting up monitoring and observability with Prometheus, Grafana, and ELK stack to ensure platform reliability with 99.9% uptime.
5. Automating deployment workflows using ArgoCD for GitOps practices, ensuring infrastructure and application consistency.
6. Designing and implementing auto-scaling solutions for handling variable workloads, particularly during peak usage times.
7. Creating and maintaining Docker images for microservices, optimizing them for faster startup and smaller footprint.
8. Implementing security best practices, including vulnerability scanning in CI/CD pipeline and secret management with HashiCorp Vault.
9. Establishing disaster recovery procedures that reduced our RTO from 4 hours to 30 minutes.
10. Mentoring junior DevOps engineers and collaborating with development teams to improve deployment practices.

1. Immediate triage:
   • Check running processes with top or htop to identify memory-consuming processes
   • Use ps aux --sort=-%mem to sort processes by memory usage
   • Run free -m to verify swap usage and available memory
2. Emergency mitigation:
   • Restart memory-hogging services if non-critical
   • Clear application caches if applicable
   • Adjust JVM heap settings for Java applications
   • In extreme cases, use the Linux OOM killer selectively with echo 1000 > /proc/[PID]/oom_adj
3. Root cause analysis:
   • Check application logs for memory leaks or unusual behavior
   • Use tools like pmap to examine memory allocation per process
   • Review recent deployments or configuration changes
   • Analyze memory usage patterns with Prometheus/Grafana metrics
4. Long-term solutions:
   • Implement vertical scaling (increase server memory)
   • Configure proper resource limits in Kubernetes/Docker
   • Set up horizontal scaling for distributed load
   • Implement memory usage alerts at 80% threshold
   • Optimize application code and database queries
   • Add caching layers where appropriate

1. Define the baseline:
   • Check historical metrics to understand normal performance
   • Quantify the current latency issue (how much slower than usual)
2. End-to-end monitoring:
   • Analyze request tracing with tools like Jaeger or Zipkin to identify slow components
   • Check Prometheus/Datadog metrics for unusual patterns
   • Review all microservices in the request path
3. Infrastructure inspection:
   • Verify CPU/memory usage on affected services
   • Check network performance between services
   • Monitor database query execution time and load
   • Inspect cloud provider status for regional issues
4. Application profiling:
   • Use APM tools to identify slow code paths
   • Check for N+1 query problems
   • Review recent deployments that might have introduced regressions
5. Specific checks:
   • Database: Review slow query logs, check for missing indexes
   • Caching: Verify cache hit rates, potential evictions
   • External APIs: Test direct calls to determine if third-party services are slow
   • Load balancers: Check for proper distribution of traffic

1. Code-level optimizations:
   • Refactor inefficient algorithms
   • Implement caching for expensive operations
   • Optimize database queries with proper indexing
   • Add connection pooling for external services
2. Infrastructure improvements:
   • Scale horizontally by adding more instances of bottlenecked services
   • Implement auto-scaling based on CPU/memory thresholds
   • Upgrade resources for vertically scaling (larger instances)
   • Move to faster storage solutions if I/O is the bottleneck
3. Architecture changes:
   • Implement asynchronous processing for non-critical operations
   • Add message queues to handle traffic spikes
   • Use circuit breakers to prevent cascading failures
   • Implement CDN for static content delivery
4. Database optimizations:
   • Add read replicas to distribute query load
   • Implement database sharding for write-heavy workloads
   • Use connection pooling to reduce connection overhead
   • Optimize slow queries identified in analysis
5. Caching strategy:
   • Implement Redis/Memcached for frequently accessed data
   • Use browser caching for frontend assets
   • Add API response caching with appropriate TTL

• Implementing a Redis cache layer for frequent AI model requests, reducing database load by 40%
• Moving CPU-intensive ML inference to dedicated, optimized instances
• Implementing a priority queue system for processing requests
• Setting up Istio service mesh to better control traffic routing and retries

1. Stop the instance (don't terminate) to preserve the EBS volumes.
2. Create EBS snapshots of all attached volumes immediately for backup.
3. Detach the root volume from the corrupted instance.
4. Launch a temporary recovery EC2 instance in the same Availability Zone.
5. Attach the corrupted volume to the recovery instance as a secondary volume:

   aws ec2 attach-volume --volume-id vol-xxxxx --instance-id i-xxxxx --device /dev/sdf

6. Mount the volume on the recovery instance to access data:

   sudo mkdir /mnt/recovery sudo mount /dev/xvdf1 /mnt/recovery

7. If mounting fails due to filesystem corruption, try filesystem repair:  
8. sudo fsck -y /dev/xvdf1
9. Extract critical data from the mounted volume to a secure location:

   sudo cp -r /mnt/recovery/path/to/critical/data /home/ec2-user/recovered-data

10. Transfer the data to S3 or EBS snapshot for safekeeping:

    aws s3 cp /home/ec2-user/recovered-data s3://my-backup-bucket/ --recursive

11. For system recovery, create a new instance from a fresh AMI and restore the data.
12. If needed, use AWS Backup or create an automated snapshot solution to prevent future data loss.

1. Performance assessment:
   • Run performance tests using Lighthouse, WebPageTest, or GTmetrix
   • Set up real user monitoring (RUM) with Datadog or New Relic
   • Analyze server response times using application logs
2. Frontend optimizations:
   • Compress and minify CSS/JS/HTML
   • Implement lazy loading for images and non-critical resources
   • Use proper image formats (WebP, AVIF) and responsive sizing
   • Implement browser caching with appropriate cache headers
   • Enable Gzip/Brotli compression
3. Backend improvements:
   • Identify and optimize slow API endpoints
   • Implement caching for frequently accessed data with Redis
   • Optimize database queries with proper indexing
   • Use connection pooling for database connections
4. Infrastructure enhancements:
   • Implement CDN for static assets (CloudFront, Cloudflare)
   • Scale application servers horizontally under load
   • Configure load balancer for proper traffic distribution
   • Upgrade server resources if CPU/memory is the bottleneck
5. Advanced techniques:
   • Implement HTTP/2 or HTTP/3 for multiplexed connections
   • Use server-side rendering or static site generation where appropriate
   • Consider edge computing for global audiences
   • Implement service worker for offline capabilities

• Uses a more structured, predefined syntax with strict hierarchical structure
• Begins with pipeline block and requires specific sections like agent, stages, steps
• Easier for beginners with less Jenkins/Groovy experience
• Self-documenting with clear syntax
• Built-in input validation that catches errors before runtime
• Provides a simplified model with limited flexibility
• Better integration with Blue Ocean visualization

groovy

pipeline {     agent any     stages {         stage('Build') {             steps {                 sh 'mvn clean package'             }         }     } }

• Based on Groovy scripting language with fewer syntax restrictions
• Begins with node block
• Offers more flexibility and control with full Groovy programming capabilities
• Better for complex logic and conditions
• Steeper learning curve requiring Groovy knowledge
• Provides programmatic flow control (if/else, try/catch)
• Runtime error checking (versus compile-time checking)

groovy

node {     stage('Build') {         try {             sh 'mvn clean package'         } catch (Exception e) {             currentBuild.result = 'FAILURE'         }     } }

1. Access Control:
   • Use role-based authentication with the Role-based Authorization Strategy plugin
   • Implement project-based matrix authorization for granular permissions
   • Restrict job modification rights to senior DevOps engineers only
   • Enforce strict credential access controls
2. Credential Management:
   • Store all secrets in Jenkins Credentials Plugin, never in job configs
   • Use credential binding for secure injection into pipelines
   • Rotate credentials regularly with automated processes
   • Implement AWS IAM roles for EC2 instances instead of static credentials
3. Pipeline Security:
   • Enable script security for Groovy sandboxing
   • Implement strict approval process for custom scripts
   • Use Jenkinsfile from version-controlled repositories only
   • Validate all external inputs and parameters
4. Infrastructure Security:
   • Run Jenkins master on private subnets with restricted access
   • Use Jenkins agents for execution with principle of least privilege
   • Implement network segmentation with security groups
   • Keep Jenkins and plugins regularly updated
5. Audit and Compliance:
   • Enable comprehensive audit logging
   • Implement build history retention policies
   • Use the Audit Trail plugin to track all user actions
   • Regular security scans of Jenkins environment

1. Install and configure Project-based Matrix Authorization Strategy plugin if not already available
2. Navigate to Jenkins dashboard > Manage Jenkins > Configure Global Security
3. Under Authorization, select "Project-based Matrix Authorization Strategy"
4. Set up global permissions (typically restrictive by default)
5. For the specific job access:
   • Go to the specific job configuration
   • Find the "Enable project-based security" checkbox and enable it
   • Add the specific user with only the required permissions:
     • Job/Read: To view the job
     • Job/Build: To trigger builds
     • Job/Workspace: To access workspace files
     • Job/Cancel: To cancel running builds if needed
6. Verify access by having the user log in and confirm they can only see and interact with the intended job
7. For managing multiple jobs with similar permissions:
   • Create a folder structure with the Jenkins Folders plugin
   • Apply permissions at the folder level
   • Place related jobs within the folder
8. Document the access granted in our access control register for audit purposes

1. I created a dedicated Git repository for our shared libraries with this structure:
   • vars/: Contains global variables/functions used in pipelines
   • src/: Houses Java/Groovy classes for complex logic
   • resources/: Stores non-Groovy files like JSON templates
2. Configured the shared library in Jenkins:
   • Set up as a "Global Shared Library" in Jenkins configuration
   • Used semantic versioning with git tags for library versioning
   • Implemented approval process for library changes

1. Standardized CI/CD Stages:
   • Created reusable functions for common pipeline stages:

     groovy
     
     // vars/standardBuild.groovy def call(Map config) {     sh "docker build -t ${config.imageName} ."     sh "docker tag ${config.imageName} ${config.registry}/${config.imageName}:${config.version}"     sh "docker push ${config.registry}/${config.imageName}:${config.version}" }

2. Security Scanning Integration:
   • Built shared functions for security tools integration:

     groovy
     
     // vars/securityScan.groovy def call(String imageName) {     sh "trivy image ${imageName} --severity HIGH,CRITICAL" }

3. Deployment Functions:
   • Created deployment helpers for different environments:

     groovy
     
     // vars/deployToK8s.groovy def call(String environment, String appName, String version) {     sh "helm upgrade --install ${appName} ./charts/${appName} --set image.tag=${version} -n ${environment}" }

4. Notification System:
   • Implemented standardized Slack/Teams notifications:

     groovy
     
     // vars/notifyBuildStatus.groovy def call(String status) {     // Logic to send appropriate notifications }

1. Check pod status and restart information:

   kubectl get pod <pod-name> -n <namespace> kubectl describe pod <pod-name> -n <namespace>

   • Look for restart count, last state, and termination reason
2. Examine pod logs:

   # Current logs kubectl logs <pod-name> -n <namespace>  # Previous container logs if it's crashed kubectl logs <pod-name> -n <namespace> --previous

3. Check for resource constraints:
   • Look for OOMKilled errors indicating memory issues
   • Review resource requests/limits in pod spec
   • Check node resource availability:

     kubectl describe node <node-name>

4. Verify health probes:
   • Check if liveness or readiness probes are failing
   • Ensure probe endpoints are responding correctly
   • Temporarily adjust probe timeouts/thresholds if needed
5. Inspect ConfigMaps and Secrets:
   • Verify the pod can access required configuration
   • Check for typos or misconfiguration
6. Check container dependencies:
   • Ensure the application can connect to databases, caches, APIs
   • Check network policies aren't blocking required traffic
7. Common solutions based on findings:
   • Increase resource limits if OOMKilled
   • Fix application bugs if logs show application errors
   • Adjust health probe configuration if too strict
   • Update environment variables if misconfigured
   • Fix image version if incompatible with environment

1. Liveness Probes:
   • Detect if a container is running but deadlocked or in a broken state
   • Trigger container restart when they fail
   • In our AI application, I implemented HTTP liveness probes for web services:

     yaml
     
     livenessProbe:   httpGet:     path: /health     port: 8080   initialDelaySeconds: 30   periodSeconds: 10   timeoutSeconds: 5   failureThreshold: 3

2. Readiness Probes:
   • Determine if a pod is ready to receive traffic
   • Failed readiness probes remove pod from service endpoints
   • For our database-dependent services, I used:

     yaml
     
     readinessProbe:   httpGet:     path: /ready     port: 8080   initialDelaySeconds: 15   periodSeconds: 5

3. Startup Probes:
   • Specifically for slow-starting containers
   • Disable liveness/readiness until the application is fully initialized
   • Critical for our ML inference services that load large models:

     startupProbe:   httpGet:     path: /startup     port: 8080   failureThreshold: 30   periodSeconds: 10

• For web services: HTTP probes checking custom health endpoints
• For database services: TCP socket probes verifying port accessibility
• For cache services: Exec probes running internal health checks

1. Preventing traffic to pods that weren't fully initialized
2. Automatically restarting deadlocked services
3. Gracefully handling temporary dependencies failures

• 3 separate clusters across different regions for high availability
• Primary production cluster: 15 worker nodes
• Secondary clusters: 8 worker nodes each
• Each node: c5.2xlarge instances (8 vCPU, 16GB RAM)
• Autoscaling configured to scale between 10-20 nodes based on demand

• Total running pods in production: ~350 pods
• Core services: 120 pods (API gateways, authentication, data services)
• AI/ML components: 80 pods (inference engines, NLP processors)
• Supporting services: 150 pods (monitoring, logging, message queues)

• Implemented node affinity to separate CPU-intensive and memory-intensive workloads
• Used pod disruption budgets to ensure service availability during upgrades
• Configured horizontal pod autoscaling based on CPU/memory usage
• Reserved capacity for system components (10% of cluster resources)

• Spread critical services across multiple AZs
• Implemented pod anti-affinity for critical services
• Maintained minimum 3 replicas for stateless services

• Average pods per node: 23-25 pods
• Maximum pods per node: 30 (resource limit enforced via kubelet)
• Minimum pods per node: 15 (system pods and critical services)

• CPU allocation: Maximum 80% of available CPU (leaving headroom)
• Memory allocation: Maximum 75% of available memory
• Network capacity: Considered for data-intensive services

1. Deployment Strategy:
   • Used Kubernetes Deployments and StatefulSets for workload management
   • Implemented GitOps with ArgoCD for application deployment
   • Maintained declarative manifests in Git repositories
   • Applied blue/green deployment patterns for zero-downtime updates
2. Resource Management:
   • Set appropriate resource requests and limits for all pods
   • Implemented Horizontal Pod Autoscaling (HPA) based on CPU/memory metrics
   • Used Pod Disruption Budgets to ensure availability during updates
   • Applied node selectors and affinity rules to optimize pod placement
3. Configuration Management:
   • Maintained environment-specific configs via ConfigMaps and Secrets
   • Used AWS Secrets Manager with External Secrets Operator for sensitive data
   • Implemented Helm charts for templating and parameterization
   • Stored application configs in version control
4. Monitoring and Observability:
   • Set up Prometheus and Grafana dashboards to monitor pod health
   • Implemented custom metrics for autoscaling ML workloads
   • Used Datadog for application performance monitoring
   • Set up ELK stack for centralized logging and log analysis
5. Network Management:
   • Implemented appropriate NetworkPolicies for pod-to-pod communication
   • Used AWS ALB Ingress Controller for external access
   • Set up service mesh with Istio for advanced traffic management
   • Configured proper security groups at the cluster level

1. Service DNS resolution:
   • The most common and recommended method
   • Use fully qualified domain name (FQDN) format:

     <service-name>.<namespace>.svc.cluster.local

   • For example, if namespace B has a service called "api-service", a pod in namespace A would connect to:

     api-service.namespaceB.svc.cluster.local:8080

2. NetworkPolicy configuration:
   • By default, all pods can communicate across namespaces
   • If NetworkPolicies are in place, they must explicitly allow cross-namespace traffic:
   • kind: NetworkPolicy apiVersion: networking.k8s.io/v1 metadata: name: allow-from-namespace-a namespace: namespaceB spec: ingress: - from: - namespaceSelector: matchLabels: name: namespaceA podSelector: {}
3. Service Export/Import:
   • For more complex setups, use a service mesh like Istio
   • Create a ServiceEntry in namespace A to explicitly import services from namespace B

1. Internet Gateway:
   • External traffic enters through AWS Internet Gateway
   • Route tables direct traffic to appropriate VPC subnets
2. Load Balancer Layer:
   • AWS Application Load Balancer (ALB) receives traffic
   • ALB performs TLS termination and initial request routing
   • AWS WAF integrated with ALB provides layer 7 protection against common attacks
3. AWS VPC/Subnet Layer:
   • Traffic flows through VPC to properly configured security groups
   • EKS nodes placed in private subnets with NAT gateways for outbound traffic
   • Security groups limit traffic to EKS node ports
4. Kubernetes Ingress Layer:
   • AWS ALB Ingress Controller translates Ingress resources to ALB configuration
   • Ingress resources define routing rules based on hostnames and paths
   • Example configuration:

     yaml
     
     apiVersion: networking.k8s.io/v1 kind: Ingress metadata:   annotations:     kubernetes.io/ingress.class: alb spec:   rules:   - host: api.aiapp.example.com     http:       paths:       - path: /         pathType: Prefix         backend:           service:             name: api-gateway             port:               number: 80

5. Kubernetes Service Layer:
   • Services distribute traffic to pods using kube-proxy
   • ClusterIP services for internal communication
   • NodePort services for ALB integration
6. Pod Network:
   • AWS VPC CNI plugin provides pod networking
   • Each pod has its own IP within the VPC CIDR
   • NetworkPolicies enforce pod-to-pod communication rules

1. Modify VPC CNI configuration:
   • Switch to custom networking mode with AWS_VPC_K8S_CNI_CUSTOM_NETWORK_CFG=true
   • Configure prefix assignment mode to reduce IP usage:

     kubectl set env daemonset aws-node -n kube-system ENABLE_PREFIX_DELEGATION=true

   • 
     
   • This allows assigning a /28 prefix per ENI instead of individual IPs, increasing IP capacity ~16x
2. VPC and subnet expansion:
   • Create new VPC with larger CIDR block (e.g., /16 instead of /20)
   • Migrate to new VPC using cluster migration techniques
   • For temporary relief, add new subnets with unused CIDR ranges
3. Optimize pod density:
   • Increase maxPods per node to maximize IP utilization
   • Use larger instance types with more ENIs for higher pod capacity
   • Implement pod consolidation to reduce total IP usage
4. Instance type adjustment:
   • Switch to Nitro-based instances with higher ENI/IP limits
   • For critical sections, use placement groups to improve network performance
5. Alternative CNI options:
   • Consider alternate CNI plugins like Calico in IPIP mode
   • This overlay approach removes dependency on VPC secondary IP ranges

1. Prometheus and Grafana Stack:
   • Deployed Prometheus Operator via Helm for metrics collection
   • Set up custom Grafana dashboards for different service categories
   • Created specific dashboards for pod resources, API latency, and error rates
   • Implemented recording rules for frequently queried metrics
2. Node and Infrastructure Monitoring:
   • Used node-exporter for hardware-level metrics
   • Integrated CloudWatch metrics for EKS control plane monitoring
   • Set up kube-state-metrics for cluster-level insights
   • Monitored EBS volumes and network interfaces
3. Application Performance Monitoring:
   • Deployed Datadog agents with APM for transaction tracing
   • Instrumented critical services with OpenTelemetry
   • Created service maps to visualize dependencies
   • Set up custom metrics for AI model performance monitoring
4. Logging Architecture:
   • Implemented Fluent Bit daemonset for log collection
   • Centralized logs in Elasticsearch (ELK stack)
   • Created Kibana dashboards for log analysis
   • Set up log-based alerts for critical errors
5. Alert Management:
   • Configured Alertmanager with different severity levels
   • Set up PagerDuty integration for critical alerts
   • Used Slack for lower priority notifications
   • Implemented alert aggregation to prevent alert storms
6. Custom Monitoring for AI Workloads:
   • Created specialized metrics for ML model inference times
   • Monitored model accuracy drift
   • Set up custom dashboards for NLP processor performance
7. SLO/SLI Tracking:
   • Defined Service Level Objectives for critical paths
   • Implemented error budgets for key services
   • Created SLO dashboards to track performance against targets

1. Node Exporter Deployment:
   • Deployed as a DaemonSet to ensure it runs on every node
   • The Node Exporter is a Prometheus exporter specifically designed to expose hardware and OS metrics
   • Configuration example:

     yaml
     
     apiVersion: apps/v1 kind: DaemonSet metadata:   name: node-exporter   namespace: monitoring spec:   selector:     matchLabels:       app: node-exporter   template:     metadata:       labels:         app: node-exporter     spec:       hostNetwork: true       containers:       - name: node-exporter         image: prom/node-exporter:v1.3.1         ports:         - containerPort: 9100           name: metrics         volumeMounts:         - name: proc           mountPath: /host/proc           readOnly: true         - name: sys           mountPath: /host/sys           readOnly: true       volumes:       - name: proc         hostPath:           path: /proc       - name: sys         hostPath:           path: /sys

2. Service Discovery:
   • Prometheus uses Kubernetes API for service discovery
   • It identifies targets through role-based configurations
   • The Prometheus server configuration includes job definitions:

     yaml
     
     scrape_configs:   - job_name: 'kubernetes-nodes'     kubernetes_sd_configs:     - role: node     relabel_configs:     - source_labels: [__address__]       regex: '(.*):10250'       replacement: '${1}:9100'       target_label: __address__       action: replace

3. Metrics Collection Process:
   • Prometheus server polls each Node Exporter endpoint at a defined interval (typically 15-30s)
   • Node Exporter exposes an HTTP endpoint (usually :9100/metrics)
   • When scraped, Node Exporter collects current metrics from the OS
   • Metrics are returned in Prometheus text-based format
4. Collected Node Metrics:
   • CPU usage and load
   • Memory utilization
   • Disk I/O and space usage
   • Network traffic and errors
   • System uptime and process counts
   • File descriptor usage
5. Storage and Processing:
   • Metrics are stored in Prometheus' time-series database
   • Data is compressed and optimized for time-series queries
   • Retention period configured based on storage capacity (7-30 days in our setup)
6. Additional Node-Level Exporters:
   • kube-state-metrics: Provides Kubernetes object metrics
   • cAdvisor: Collects container metrics (built into kubelet)
   • Custom exporters for specific applications
7. Handling Node Changes:
   • Prometheus dynamically updates targets when nodes are added/removed
   • Relabeling rules standardize metrics across heterogeneous nodes
   • Service discovery refreshes at configurable intervals

1. Verify the outage scope:
   • Check if the issue affects all users or specific segments
   • Determine if it's isolated to one service or impacting multiple components
   • Verify if it's environment-specific (prod vs. staging)
2. Check monitoring dashboards first:
   • Review Grafana dashboards for spikes in error rates
   • Check service health metrics in Prometheus
   • Examine Datadog APM for transaction traces and errors
   • Look for correlated events in other services
3. Investigate logs:
   • Query ELK stack for error messages:

     
     kubernetes.namespace: "app-namespace" AND log: "error" AND kubernetes.pod.name: "app-*"

   • Check for specific HTTP error codes in access logs
   • Look for application exception stacktraces
4. Kubernetes-specific checks:
   • Verify pod status and restart counts:
   • kubectl get pods -n <namespace> | grep <app-name> kubectl describe pod <pod-name> -n <namespace>
   • Check pod logs:

     kubectl logs <pod-name> -n <namespace> --tail=100

   • Verify service endpoints are correctly registered:

     kubectl get endpoints <service-name> -n <namespace>

5. Infrastructure validation:
   • Check node resources (CPU/memory pressure)
   • Verify network connectivity between services
   • Check if AWS/GCP services are experiencing outages
   • Validate if backing services (databases, caches) are responsive
6. Debugging and resolution:
   • If needed, access a problematic pod directly:

     kubectl exec -it <pod-name> -n <namespace> -- /bin/bash

   • Test connectivity to dependent services
   • Check application configuration
   • Validate recent changes through deployment history
7. Resolution approaches:
   • Rollback to previous working version if recent deployment caused issue
   • Scale up resources if under load pressure
   • Restart affected services if temporary state issues are suspected
   • Apply configuration fixes if misconfiguration identified