Uncodemy DevOps Course Placement Support Explained

Ask the counsellor about DevOps placement support:
1. How many resume reviews are included?
2. Who conducts mock interviews, and are they technical?
3. How many mock interviews can I take?
4. Is my GitHub reviewed one-to-one?
5. How are interview opportunities shared?
6. What is expected from me to stay eligible?
7. Can I repeat a batch if I fall behind?

I am ready for placement support when I can:
- explain my capstone pipeline end to end
- write a Dockerfile and a K8s deployment from memory
- troubleshoot a failing pod using logs and describe
- explain Terraform plan vs apply
- show all projects on GitHub with READMEs

# Prometheus alert: pod restarting too often
groups:
  - name: app-alerts
    rules:
      - alert: PodRestartingOften
        expr: increase(kube_pod_container_status_restarts_total[15m]) > 3
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Pod {{ $labels.pod }} restarted more than 3 times in 15 minutes"

Incident (lab): pod stuck in CrashLoopBackOff
Check: kubectl describe pod web-7d9...  -> exit code 1
Logs:  kubectl logs web-7d9... --previous
Cause: missing DATABASE_URL environment variable
Fix:   added the variable via a Secret, redeployed
Prevention: readiness probe + config validation at startup

Use this structure for scenario questions:
1. Clarify: what changed, when, who is affected?
2. Check: dashboards, logs, recent deployments
3. Contain: roll back or scale to restore service
4. Find cause: logs, events, diffs, metrics
5. Fix: permanent change, reviewed and tested
6. Prevent: alert, test or runbook so it is caught earlier

# Practise these until they feel natural
df -h                         # disk usage by filesystem
du -sh /var/log/* | sort -h   # largest log folders
journalctl -u nginx --since "1 hour ago"
ss -tulpn                     # listening ports and processes
top -o %MEM                   # memory-hungry processes
systemctl status docker

DevOps Projects
- Built a CI/CD pipeline (GitHub Actions) that tests, builds a
  Docker image and deploys to Kubernetes on every push.
- Provisioned VPC, subnets and an EKS-style cluster lab with
  Terraform; documented plan/apply/destroy workflow.
- Set up Prometheus and Grafana with alert rules for pod
  restarts and high CPU.
Skills: Linux, Git, Docker, Kubernetes, Terraform, CI/CD, AWS

Practise answering out loud (2 minutes each):
1. A deployment failed in production. What do you do first?
2. Disk is full on a server. How do you find the cause?
3. A pod is in CrashLoopBackOff. Walk me through debugging.
4. How would you roll back a bad release?
5. How do you keep secrets out of Git?

Item                              Status
Capstone pipeline working         [ ]
Capstone README + diagram         [ ]
Terraform project                 [ ]
Monitoring project                [ ]
Troubleshooting log (3 entries)   [ ]
Resume reviewed                   [ ]
Mock interviews completed         ___ / ___