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How I Passed the Certified Kubernetes Administrator (CKA) Exam: My Experience

2026-08-14T12:02:53.987-07:00

Hi there! Recently, I took the Certified Kubernetes Administrator (CKA) exam. In this blog post, I want to share my experience and provide some tips and advice for anyone who is planning for the CKA exam.

As we know CKA certification is one of the challenging exams that tests your knowledge of Kubernetes, cluster troubleshooting, and helps improve your skills as a DevOps engineer. The CKA certification is highly respected in the industry and demonstrates a high level of expertise in managing Kubernetes clusters.

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Exam Overview

The CKA exam is a hands-on, practical exam that tests your ability to manage Kubernetes clusters. The exam lasts for 2 hours and consists of multiple tasks that you must complete using a live Kubernetes cluster. The tasks cover a wide range of topics, including cluster architecture, networking, storage, security, and troubleshooting.

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Study Tips: Here are some tips that helped me prepare for the CKA exam:

Use multiple sources of study materials: In addition to the official Kubernetes documentation, I found the training materials from KodeKloud (Mumshad Mannambeth) to be very helpful.
Practice, practice, practice: The CKA exam is very hands-on, so it’s important to get as much practice as possible. Set up your own Kubernetes cluster and try to complete all the tasks from the exam syllabus.
For practicing and preparing for the CKA mock exam, I used the courses from the KodeKloud mock series and Alok Kumar. These courses provided me with valuable knowledge and hands-on experience that helped me succeed in the exam.

Links

https://kodekloud.com/courses/ultimate-certified-kubernetes-administrator-cka-mock-exam/

https://www.udemy.com/course/certified-kubernetes-administrator-cka-lab-course/

CKA study Tracker I used this to track my progress on CKA topics.

Extra Resource: I used this book to get some clarity and deep dive into topics. Kubernetes Bible

Exam Advice: Here is some advice for taking the CKA exam:

Manage your time carefully: The CKA exam is 2 hours long, so it’s important to manage your time carefully. Make sure you leave enough time to complete all the tasks.
Read the questions carefully: Make sure you understand what each task is asking you to do before you start working on it.
Use the official Kubernetes documentation: During the exam, you are allowed to access the official Kubernetes documentation. Make sure you know how to quickly find the information you need.
There are some questions on the CKA exam that follow a similar pattern and are repeated in various resources. Make sure you practice these types of questions using the courses from KodeKloud and Alok Kumar, as mentioned earlier. Avoid making mistakes on these questions, as it could be costly.

Passing the CKA exam was a challenging but rewarding experience. I hope that my tips and advice will help others who are preparing for the exam. If you have any questions or would like to connect with me, feel free to reach out on LinkedIn.

My other blog

10 Books Every DevOps Engineer Should Read

Free Terraform Quiz !! Check your Score

👉 Preparing for DevOps interviews? Check our 100+ Scenario Question Bank to practice real incident playbooks.

The 3-Week Schedule for AWS Certified CloudOps Engineer - Associate

2026-08-13T09:28:13.422-07:00

The 3-Week Schedule for AWS Certified CloudOps Engineer - Associate

Week 1 — Monitoring and Reliability (44% of the exam)

Day 1 — CloudWatch fundamentals Metrics, namespaces, dimensions, statistics vs. percentiles. Default EC2 metrics vs. what needs the CloudWatch agent (memory and disk usage are not default — this shows up constantly). Custom metrics, resolution, retention.

Day 2 — Logs and audit trail CloudWatch Logs, log groups, retention settings, metric filters, subscription filters, Logs Insights query syntax. CloudTrail: management vs. data events, organization trails, log file validation. Know when the answer is CloudTrail vs. CloudWatch Logs vs. VPC Flow Logs.

Day 3 — Alarms and auto-remediation Alarm states, missing-data treatment, composite alarms. EventBridge rules and targets. SNS. Then the remediation side: Systems Manager Automation runbooks, Lambda targets, EC2 recovery actions. This is where domain 1 gets scenario-heavy.

Day 4 — Auto Scaling and load balancing Launch templates vs. launch configs, target tracking vs. step vs. scheduled scaling, cooldowns, lifecycle hooks, warm pools. ALB vs. NLB vs. GWLB, target groups, health check tuning, stickiness, cross-zone balancing.

Day 5 — High availability Multi-AZ vs. multi-Region. RDS Multi-AZ vs. read replicas (different purposes — the exam checks that you know which). Aurora failover. S3 Cross-Region Replication. Route 53 health checks and failover routing.

Day 6 — Backup and recovery AWS Backup plans and vaults, EBS snapshots and the incremental model, AMI lifecycle, Data Lifecycle Manager. RTO/RPO and matching the four DR strategies to them. S3 versioning, lifecycle policies, Object Lock.

Day 7 — Consolidate No new material. 50–60 practice questions on domains 1 and 2 only, then re-read the docs for everything you got wrong.

Week 2 — Automation, Security, Networking (56%)

Day 8 — CloudFormation Template anatomy, intrinsic functions (Ref, GetAtt, Sub, FindInMap), parameters and mappings, conditions. Then the operational parts the exam loves: change sets, drift detection, DeletionPolicy, UpdateReplacePolicy, nested stacks, StackSets across accounts and Regions, and which update types cause replacement.

Day 9 — Systems Manager The single densest service on this exam. Parameter Store (Standard vs. Advanced, SecureString), Session Manager and why it beats bastion hosts, Patch Manager and patch baselines, Run Command, State Manager, Automation runbooks, Inventory. Know the SSM Agent prerequisites and the IAM instance profile it needs.

Day 10 — Deployment and containers Rolling, blue/green, canary, immutable. Elastic Beanstalk deployment policies. Then the SOA-C03 additions: ECR basics, EKS operational concepts, and where CDK and Terraform fit relative to CloudFormation.

Day 11 — IAM Policy evaluation logic and explicit deny. Identity vs. resource policies. Roles and cross-account access. Permission boundaries. SCPs and how they interact with IAM policies. IAM Identity Center. Practise reading a policy document and saying exactly what it allows.

Day 12 — Security services and cost KMS key types, rotation, grants. Secrets Manager vs. Parameter Store. GuardDuty, AWS Config with conformance packs and remediation, Inspector, Security Hub, Trusted Advisor — know which one answers which question. Then cost: Cost Explorer, Budgets, Savings Plans vs. Reserved Instances, S3 storage classes and Intelligent-Tiering, Compute Optimizer.

Day 13 — VPC Subnets and route tables, IGW vs. NAT Gateway, Security Groups vs. NACLs (stateful vs. stateless — reliably tested), VPC endpoints gateway vs. interface, peering and its limits, Transit Gateway, VPC Flow Logs and reading a rejected-traffic line. Expect multi-step troubleshooting scenarios here.

Day 14 — DNS and edge, then consolidate Route 53 routing policies — simple, weighted, latency, failover, geolocation, geoproximity, multivalue — and alias vs. CNAME. CloudFront behaviours, OAC, cache invalidation. Finish with 50–60 questions across domains 3, 4 and 5.

Week 3 — Mocks and repair

No new content this week. The whole point is finding your gaps while there's still time to close them.

Day 15 — Mock exam 1. Full 130 minutes, timed, no pausing, no looking anything up. Score it and note the per-domain breakdown.

Day 16 — Review every single wrong answer. Not just the right option — why each distractor is wrong. For anything you don't fully follow, read the actual AWS documentation page. Slower than it sounds; budget the full two hours.

Day 17 — Mock exam 2.

Day 18 — Review, then drill your weakest domain. By now a pattern is obvious. Mine was CloudFormation update behaviour and Route 53 routing policies.

Day 19 — Mock exam 3. You want to be consistently above 80% here. The real exam pass mark is 72%, but mock scores tend to run a little optimistic relative to it.

Day 20 — Read the official exam guide task statements end to end. Every bullet AWS lists is fair game. Anything you can't explain in a sentence, go look up. Flashcards for service limits and defaults.

Day 21 — Light review and stop. Skim your notes, confirm your Pearson VUE booking and ID, and finish early. Cramming the night before buys nothing on a scenario exam.

How to Fix Kubernetes CrashLoopBackOff

2026-02-08T10:34:00.000-08:00

If your Pod is stuck in CrashLoopBackOff, it means Kubernetes is repeatedly starting the container, but the container keeps crashing. After a few failed restarts, Kubernetes applies a backoff delay and shows the status CrashLoopBackOff.

In this guide, you’ll learn the exact commands to debug CrashLoopBackOff and the 7 most common real-world causes with fixes.

What is CrashLoopBackOff in Kubernetes?
Quick Commands to Debug CrashLoopBackOff
Fix #1: Application is crashing on startup
Fix #2: Wrong command or entrypoint
Fix #3: Missing environment variables / secrets
Fix #4: Liveness probe killing the pod
Fix #5: Image issues or wrong architecture
Fix #6: OOMKilled (memory limit exceeded)
Fix #7: Volume mount or permission issues
Best Practices to Prevent CrashLoopBackOff

1) What is CrashLoopBackOff in Kubernetes?

CrashLoopBackOff is not the error itself.
It is Kubernetes telling you:

“Your container keeps crashing. I will restart it, but with increasing delay.”

So your real job is to find why the container exits.

2) Quick Commands to Debug CrashLoopBackOff

Step 1: Check pod status

kubectl get pods -n <namespace>

Step 2: Describe the pod (most important)

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

Look at:

Events section
Container restart count
Probe failures
OOMKilled

Step 3: Check logs

kubectl logs <pod-name> -n <namespace>

If the pod restarted, check previous container logs:

kubectl logs <pod-name> -n <namespace> --previous

Step 4: Check container exit code

kubectl get pod <pod-name> -n <namespace> -o jsonpath='{.status.containerStatuses[*].state.terminated.exitCode}'

3) Fix #1: Application is Crashing on Startup

Symptoms

Logs show stack trace
Exit code is often 1
Pod restarts continuously

Fix

Check the logs:

kubectl logs <pod-name> -n <namespace> --previous

Then fix the app issue:

missing config file
invalid config format
app cannot connect to DB
missing dependency

4) Fix #2: Wrong Command or Entrypoint

This is extremely common in Docker + Kubernetes.

Symptoms

Logs show:

exec format error
command not found
container exits instantly

Check your deployment YAML

kubectl get deploy <deploy-name> -n <namespace> -o yaml

Look for:

command: ["..."]
args: ["..."]

Fix

Remove incorrect command/args
Ensure the binary exists in image
Verify Dockerfile ENTRYPOINT and CMD

5) Fix #3: Missing Environment Variables / Secrets

Symptoms

Logs show:

ENV VAR not set
unable to load config
permission denied to secret

Check env vars

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

Fix

If using secret:

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

Also confirm secret is referenced correctly:

envFrom:
  - secretRef:
      name: my-secret

6) Fix #4: Liveness Probe Killing the Pod

Many people confuse probe failure with “app crash”.

Your container might be running, but Kubernetes kills it due to liveness probe failures.

Symptoms

Events show:

Liveness probe failed
Readiness probe failed

Check pod events

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

Fix

Increase initialDelaySeconds
Increase timeoutSeconds
Fix health endpoint

Example:

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

7) Fix #5: Image Issues or Wrong Architecture

Symptoms

exec format error
Works on local, fails in cluster
Image pulled successfully but crashes instantly

Fix

Check node architecture:

kubectl get nodes -o wide

If your nodes are amd64 but image is arm64, container will fail.

Solution:

Build multi-arch image
Or build image for correct architecture

8) Fix #6: OOMKilled (Memory Limit Exceeded)

This is one of the most common reasons in production.

Symptoms

Exit code often 137
Pod restarts
Events show OOMKilled

Check pod status

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

Fix

Increase memory limit:

resources:
  requests:
    memory: "256Mi"
  limits:
    memory: "512Mi"

Or optimize application memory usage.

9) Fix #7: Volume Mount or Permission Issues

Symptoms

container crashes when writing to a directory
logs show permission denied
errors related to volume mount paths

Check volume mount in YAML

kubectl get pod <pod-name> -n <namespace> -o yaml

Fix

Ensure mountPath is correct
Use correct securityContext
If using non-root container, fix permissions

Example:

securityContext:
  runAsUser: 1000
  fsGroup: 1000

10) Best Practices to Prevent CrashLoopBackOff

✅ Always use readiness + liveness probes correctly
✅ Add proper resource requests/limits
✅ Validate config before deploy
✅ Use structured logs
✅ Use kubectl logs --previous during debugging
✅ Add alerting for restart count

11) FAQ

Q1. How long does CrashLoopBackOff last?

It continues until:

the pod becomes healthy
or you delete/scale down deployment

Q2. How do I stop CrashLoopBackOff immediately?

Scale deployment to 0:

kubectl scale deploy <deploy-name> -n <namespace> --replicas=0

Q3. What is the fastest way to debug?

Run:

kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --previous

Conclusion

CrashLoopBackOff is common in Kubernetes, but it is always solvable if you debug systematically.

Start with:

kubectl describe pod
kubectl logs --previous
Events + exit codes

Then apply the fix based on the root cause.

Kubernetes for Begineers [PDF Notes]

2025-07-12T20:58:00.000-07:00

ZOOM Referall form

2024-10-18T12:47:00.000-07:00

Step 1: check JOB ID from zoom career page https://careers.zoom.us/jobs/search for any matching role

without JOB ID don't fill.

***IMP*** After referall you will recieve email from zoom. Make sure to fill that with all details then only referall will be counted.

Dont apply directly or via other method otherwise referall wont be counted.

If you have already applied recently within 3 month, don't fill this form.

Google Form link [Upload CV here]

Loading…https://docs.google.com/forms/d/e/1FAIpQLScKfxLpr5y8fT8OC-hjiEo9CMAwMqzKfY4Y4QQJnf1RcW-PPw/viewform

Email monu515372@gmail.com for support/issue.

Other Post

Cloud and DevOps material & Interview questions

https://techzonein.blogspot.com/2023/08/cloud-material.html

10 Books Every DevOps Engineer Should Read

2023-08-30T11:49:00.005-07:00

Introduction:

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the systems development life cycle and provide continuous delivery with high quality. DevOps engineers are responsible for implementing and maintaining these practices.

There are many great books available on DevOps. Here are 10 of the best books that every DevOps engineer should read:

The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations by Gene Kim, Jez Humble, Patrick Debois, John Allspaw, and John Willis. This book is considered to be the "DevOps bible" and is a comprehensive guide to the principles and practices of DevOps.
Opens in a new window www.amazon.in
The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, and George Spafford. This book is a fictional story that illustrates the benefits of DevOps through the eyes of a fictional IT manager.
Opens in a new window www.amazon.in
The Unicorn Project: A Novel About DevOps, Data Science, and Building Unicorns by Gene Kim, Kevin Behr, and George Spafford. This book is a sequel to The Phoenix Project and explores the challenges of implementing DevOps in a large enterprise.
Opens in a new window www.amazon.in
Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations by Nicole Forsgren, Gene Kim, and Jez Humble. This book is a research-based study of the factors that contribute to high-performing DevOps organizations.
Opens in a new window www.amazon.in
Effective DevOps: How to Build a Dependable, Agile, and Secure Organization by Jennifer Davis and Ryn Daniels. This book provides practical advice on how to implement DevOps principles in your organization.
Opens in a new window www.amazon.in
Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation by Jez Humble and David Farley. This book teaches you how to automate the software delivery process to improve reliability and efficiency.
Opens in a new window www.amazon.com
The DevOps Adoption Playbook: A Guide to Adopting DevOps in a Multi-Speed IT Enterprise by Sanjeev Sharma. This book provides a roadmap for organizations that are looking to adopt DevOps.
Opens in a new window www.amazon.in
Building Microservices: Designing Fine-Grained Systems by Martin Fowler. This book teaches you how to design and build microservices, which are a key architectural pattern for DevOps.
Opens in a new window samnewman.io
Infrastructure as Code: Managing Servers in the Cloud by Kief Morris. This book teaches you how to manage your infrastructure using code, which is a key practice of DevOps.
Opens in a new window www.amazon.in
Kubernetes Bible by Kelsey Hightower, Brendan Burns, and Joe Beda.
Opens in a new window www.amazon.in

Conclusion:

These are just a few of the many great books available on DevOps. The best book for you will depend on your experience level and your specific interests. I hope this list helps you find the perfect book to help you learn more about DevOps and become a more effective DevOps engineer.

I hope this helps!

Cloud & DevOPS Material

2023-08-27T06:55:00.012-07:00

DevOps material for interview

https://drive.google.com/drive/folders/1fBSPB2qZNzxvnhfiwMb9sfwdoJfgfALF?usp=sharing

CKA study Tracker source Udemy

https://docs.google.com/spreadsheets/d/1R3O2EpFTJdE2JtyzTDdTBFAuBfGTcq4Er809NlKUGnM/edit?usp=sharing

DevOPS Roadmap

https://techzonein.blogspot.com/2022/05/how-to-become-devops-engineer-roadmap.html

Referall

https://techzonein.blogspot.com/2024/10/referall-for-devops-role.html

Kubernetes Workloads (Deployments, Jobs, CronJobs, etc.)

2023-07-01T11:40:00.002-07:00

Deployments

In Kubernetes, Deployments provides updates for pod and replica sets. It is basically a tool to manage how the pod will behave inside the cluster.

Once we declare the desired state in the YAML file, the deployment controller makes sure the actual state to desired

Benefits of Deployment

It provides a feature of rollback in case the current version is not stable.
We can scale up the application in case of an increased workload.

Creating a Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

In this example, we are creating a deployment named “nginx-deployment ” which is having the label “app:nginx”. The deployment will create 3 replicas in this case.

The spec selector will define how the created ReplicaSet finds which Pods to manage. It will basically match the label app: nginx and identify which pods are managed by it.

In the template section, we are defining the metadata about the pods like labels, containers name, images.

To create the deployment we can run this command.

kubectl apply -f https://k8s.io/examples/controllers/nginx-deployment.yaml

Run kubectl get deployments to check if the Deployment was created.

NAME               READY   UP-TO-DATE   AVAILABLE   AGE
nginx-deployment   0/3     0            0           1s

Statefulset

It is a type of workload in Kubernetes, which is used to manage stateful applications. This helps in managing the deployment and set of pods by providing guarantees of the ordering and uniqueness of the pods.

Statefulsets are useful for stateful applications such as databases that require persistent storage and network.

It is useful for application that requires an ordered rolling update feature.

Example

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: web
spec:
  selector:
    matchLabels:
      app: nginx # has to match .spec.template.metadata.labels
  serviceName: "nginx"
  replicas: 3 # by default is 1
  minReadySeconds: 10 # by default is 0
  template:
    metadata:
      labels:
        app: nginx # has to match .spec.selector.matchLabels
    spec:
      terminationGracePeriodSeconds: 10
      containers:
      - name: nginx
        image: registry.k8s.io/nginx-slim:0.8
        ports:
        - containerPort: 80
          name: web
        volumeMounts:
        - name: www
          mountPath: /usr/share/nginx/html
  volumeClaimTemplates:
  - metadata:
      name: www
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: "my-storage-class"
      resources:
        requests:
          storage: 1Gi

In this example, we are creating a stateful set named “web”. It will have 3 replicas. The template section will contain the spec about the pods which will be created. It mains the unique identity of each of the pods and data will store in volume mount and saved even pod got deleted or recreated.

Daemonsets

A Daemonset will make sure that each node will create a copy of a pod. Whenever a new node is added to the cluster pod will be created and if we delete a node pod created on that node will also be deleted with it.

Features of Daemonsets:

It is used by logging agents to collect logs of every node.

It is used for node monitoring purposes and ensuring security policy purposes

Example

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-elasticsearch
  namespace: kube-system
  labels:
    k8s-app: fluentd-logging
spec:
  selector:
    matchLabels:
      name: fluentd-elasticsearch
  template:
    metadata:
      labels:
        name: fluentd-elasticsearch
    spec:
      tolerations:
      # these tolerations are to have the daemonset runnable on control plane nodes
      # remove them if your control plane nodes should not run pods
      - key: node-role.kubernetes.io/control-plane
        operator: Exists
        effect: NoSchedule
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      containers:
      - name: fluentd-elasticsearch
        image: quay.io/fluentd_elasticsearch/fluentd:v2.5.2
        resources:
          limits:
            memory: 200Mi
          requests:
            cpu: 100m
            memory: 200Mi
        volumeMounts:
        - name: varlog
          mountPath: /var/log
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log

In this example, we are creating DaemonSet with the name “fluentd-elasticsearch” with labels as k8s-app: fluentd-logging.

It will match the label “name: fluentd-elasticsearch” and manages them .

The template section of Yaml will be the metadata of the pods that will be created.

Toleration is defined so that if the pod will be created on the control plane node.

The containers section of YAML will contain names and images of container-related details. Limit and resource requests are configured to be used by the pod. Volume mounts details are provided such that it mounts on the host path i.e. the node on which the pod is running and uses the path /var/log.

Cron Jobs

A cronjob creates jobs that are scheduled. It is useful for taking backups, report generation, log rotation, and so on.

Example

apiVersion: batch/v1
kind: CronJob
metadata:
  name: hello
spec:
  schedule: "* * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: hello
            image: busybox:1.28
            imagePullPolicy: IfNotPresent
            command:
            - /bin/sh
            - -c
            - date; echo Hello from the Kubernetes cluster
          restartPolicy: OnFailure

In this example, we are creating a CronJob with the name “Hello” which will be scheduled to run every minute. The templated section will contain the metadata of pods created so basically we are creating a container of image busybox which run commands of date and Hello from the Kubernetes cluster.

Jobs

A job will create one or more pods and run specific tasks on it and the job controller will monitor the status of running jobs and mark it complete once the task is complete.

If a pod fails, the Job controller can automatically restart it until the desired number of successful completions is reached.

Example

apiVersion: batch/v1
kind: Job
metadata:
  name: pi
spec:
  template:
    spec:
      containers:
      - name: pi
        image: perl:5.34.0
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never
  backoffLimit: 4

In this example, we are creating a Job with the name pi. The template section will contain the details about the container name and image and the command to run once the pods start.

The backoff limit is nothing but for handling job failures by setting a number of retries it will be for the pods.

Linux Services KodeKloud Engineer Task Success

2023-01-21T11:30:00.002-08:00

As per details shared by the development team, the new application release has some dependencies on the back end. There are some packages/services that need to be installed on all app servers under Stratos Datacenter. As per requirements please perform the following steps:

a. Install cups package on all the application servers.

b. Once installed, make sure it is enabled to start during boot.

ssh tony@stapp01. (then enter password)

sudo su -

sudo yum install cups -y

sudo systemctl start cups

sudo systemctl enable cups

sudo systemctl status cups

repeat for all servers with the same commands

Karpenter Vs Kubernetes Cluster Autoscaler

2023-01-13T06:42:00.004-08:00

What is Karpenter?
Karpenter is open source, high-performance Kubernetes cluster auto scaler which can basically schedule the right kind of compute resources to your cluster in response to changing application load.
With the usage of Karpenter in your cluster you can improve efficiency and cost of running workload.
Basically, once you configure Karpenter in your cluster it will observe the requests of unscheduled workload which is marked unschedulable. It will evaluate and take the decision to schedule the load based on parameters like node selectors, resource requests, and provision nodes that meet the requirement of the workload.
It also helps by terminating the nodes which are no longer needed. Moreover, all this activity of node autoscaling will be happening automatically for you once the setup is done.
Karpenter manages each instance directly without using node groups. This allows it to retry in seconds once capacity is not available and leverages the different types of instances available in the cloud platform.
Example: The usage of spot instances and varying compute-optimized instance types, on-demand, etc basically helps in scheduling your workload efficiently and saves on unexpected cloud bills too.
What is Kubernetes Cluster Autoscaler?
Kubernetes Cluster Autoscaler is the utility using which automatic adjustment of nodes is possible when the pod fails or is rescheduled to other nodes based on node utilization metrics. It is used to get high availability.
It works automatically and there is no manual creation required whenever new nodes are required for the workload.
How Karpenter works?
Karpenter works with the Kubernetes scheduler by observing the incoming pods, while it is usual and enough capacity is present Kubernetes scheduler will work normally and schedule the pods. When pods cannot be scheduled in the current capacity of the cluster then Karpenter comes into the picture and by overtaking the Kubernetes scheduler it works directly with cloud compute service (Example Amazon EC2 ) to provision the right node instances and schedule the workloads on them. Once pods are removed or rescheduled it looks for feasibility to terminate the nodes.
Thank you
Tripura Kant
https://www.linkedin.com/in/tripurakant/

DevOps Series-Day2- Become Linux Pro

2022-10-14T03:26:00.009-07:00

Pre-requisites:

Oracle VM VirtualBox

Download Iso Image of CentOS 7

About Linux OS

Just like Windows, iOS, and Mac OS, Linux is an operating system.
An operating system is a software that manages all of the hardware resources associated with your desktop or laptop. To put it simply, the operating system manages the communication between your software and your hardware. Without the operating system (OS), the software wouldn’t function.
It is an open-source operating system. The source code of Linux is easily available to everyone.
Linux provides security.
Customizations are possible in Linux.
Free to use.
The cost of Linux is low.
Has large community support.

Boot Processing

BIOS: Basic Input/output system executes MBR

MBR: Master Boot Record executes GRUB

GRUB: Grand Unified Bootloader executes the kernel

KERNEL: Kernel executes init/sbin

INIT Init executes run-level programs

runlevel Run level programs execute from /etc/RC

Different shells we used in Linux

Bash shell
Tcsh shell
Ksh shell
Zsh shell
Fish shell

Some of the basic Linux commands

cd Change directory
ls listing of files/directory
ls -l long listing of files/directory
echo used of reading
chmod change directory
PWD present working directory
uptime to display information about uptime
df -h disk utilization
du -sh disk usage
rm: to remove a file

File permission in Linux

File permission in Linux is associated with three categories users, group and others

-rw-r--r--

Different file permission associated are

r read 4

w write 2

x execute 1

4+2+1 = 7

For example

permission 777 signifies that

user, group, and others have all permissions i.e read write and execute

2. permission 467 represents

user has read the permission

group has read and read and write permission

others have all read, write and execute permission

Package manager

Yum vs APT

Yum is used with the RedHat family

APT is used with the ubuntu family

Both are used as dependency manager used to update, download and install software and packages into the system.

Thanks for reading

Tripura Kant

DevOps Series-Day1-DevOps Intro

2022-10-09T01:34:00.009-07:00

DevOps and Cloud Engineering are currently booming at the moment. The number of companies looking for skilled professionals in this field has jumped drastically. Let us try to understand some Basics of DevOps and Cloud Engineering.

What is DevOps?

DevOps is a cultural practice within an organization that involves automating and integrating processes between the software development and operation team. It emphasized more on team collaboration and technology automation.

It basically helps increase an organization's ability to deliver applications and services at high velocity.

DevOps is not about learning a tool and implementing that, rather it is a mindset that we have discussed below.

Implementation of DevOps

If anyone wants to implement DevOps in an organization, you need to keep in mind some basic points.

Core Values

Core Ideas

Methods

Practices

Tools

DevOps is basically a mindset that involves different teams having similar goal-set like pushing features to production involving each other streamlining the process and coming to a conclusion.

Need of Learning DevOps

Let us see If you implement DevOps within your organization, some of the benefits you can get.

Deploy faster - (Even 50 % faster)

less failure

Better recovery time

DevOps CAMS

CAMS is a philosophy oF DevOps. Let us understand this in detail.

C stands for Culture

A Stands for Automation

M stands for Measurement

S stands for Sharing

Culture- One of the Cultures which DevOps provides is talking to each other. Having discussions and taking ideas related to issues or implementation.

One of the common terminologies is People over Process over Tools. An example of that could be you cannot come to a conclusion the next day to introduce a new tool within the system as there could be already a system existing with certain processes and tools involved which could be broken down immediately because of your tool implementation.

Automation- Automation is not just like introducing and tool rather you need to see how much can you automate and does it require to do automation or not.

Therefore, it is essential to understand those points and then introduce tools wherein necessary. Like Terraform is a great tool that helps you automate your resource provisioning and replicate various resources.

Measurement- One of the statements apt here is Measure to improve. Here it is not about infra measurement only, it is also beneficial to take necessary actions based on that to improve the system.

Sharing- Share the GOAL

It is necessary to share responsibility and ownership within teams and have a mindset on how to improve that should be the mindset all DevOps people should have.

DevOps 3 ways

flow thinking

amplify feedback

experiment and learn

Let us deep dive to understand these methodologies, flow thinking is like the Dev team pushing the code to a particular branch and the code deployment will be taken care of in a release cycle like in 15 days or 30 days.

In amplify feedback, in these before any release discussion with ops and dev team and give feedback between teams about the deployment feasibility.

In experiment and learn as the name suggests there would be changes and learning in deployment process delivery.

Usually, the second way i.e amplifying feedback is the way generally practices a lot within the organization.

Big Overview to Remember in DevOps Implementation

People over process over tools

Continuous delivery

lean management

Infrastructure as code

Lets us go through these terms one by one.

People over process over tools, these we already discussed like in DevOps methodology to emphasize more on collaboration and people opinion and discussion over tools.

Continuous delivery- It stands for delivering products as quickly as possible and multiple times as possible.

Lean management- It implies minimizing the management people and making teams in sync and reporting to similar management for dev and ops.

Infrastructure as code- This is the maintaining of your infrastructure in code so that it is easy to provision and replicate it also.

DevOps Lifecycle

These are the various stages that we are followed within the organization for the delivery of products starting from plan>code>build>test>release>deploy>operate>monitor.

DevOps Terminology

Provisioning

It means making a server ready with OS, software, networking, or other dependencies.

Deployment

It is adding or upgrading software on the server.

Orchestration

Coordinating operations on multiple systems. For example, i.e Kubernetes is a container orchestration tool.

Idempotent

It means executing multiple times but getting the same result/OUTPUT in return

Continuous Integration

Methodology in which build and unit testing at every check-in

Continuous Delivery

It is deployed on the production live environment at every check-in

Thanks for reading !!! All the best in your DevOps journey.

Tripura Kant

https://www.linkedin.com/in/tripurakant/

Could not resolve host : Digital ocean Droplet issue resolved

2022-09-03T07:43:00.012-07:00

Issue

If you are facing an issue in Digital Ocean VM, follow these tips to resolve the same.

unable to resolve host Digital Ocean

fatal: unable to access 'https://github.com/in4it/prometheus-course.git/': Could not resolve host: github.com

Solution

Click on your droplets to log in to the digital ocean VM machine.

Then go to the Networking section and scroll to Firewalls

Add the inbound and outbound rules as shown

Save this and try connecting again it should work now.

Extras:

For login use root@ipadress then enter the password after the prompt example : ssh root@198.199.69.217

root@198.199.69.217's password:

How to Become DevOps Engineer| Roadmap for DevOps Engineer[2026] Updated

2022-05-24T10:12:00.032-07:00

Whether you are transitioning from a traditional IT background or pivoting from software development, becoming a DevOps Engineer in 2026 requires a structured, skills-based approach. This guide provides a comprehensive, up-to-date roadmap covering the core competencies, tools, certifications, and learning resources you need to launch or accelerate your DevOps career.

What is DevOps Engineering?

According to Atlassian, "A DevOps engineer is an IT generalist who should have wide-ranging knowledge of both development and operations, including coding, infrastructure management, system administration, and DevOps toolchains. DevOps engineers should also possess interpersonal skills since they work across company silos to create a more collaborative environment."

In practice, a DevOps Engineer acts as the connective tissue between development and operations teams — automating workflows, managing cloud infrastructure, enforcing observability standards, and enabling rapid, reliable software delivery through CI/CD pipelines. The role demands both technical depth and cross-functional collaboration.

Why DevOps? Market Demand & Salary Outlook

DevOps continues to be one of the most in-demand engineering roles globally. Organizations across all verticals — from fintech to e-commerce — are investing heavily in platform engineering and DevOps transformation. Compensation reflects this demand:

Entry-level (0–2 years): ₹4–8 LPA (India) / $80,000–$100,000 (US)
Mid-level (3–5 years): ₹12–20 LPA (India) / $110,000–$140,000 (US)
Senior/Staff (5+ years): ₹25–50 LPA (India) / $150,000–$200,000+ (US)

Roles such as Platform Engineer, Site Reliability Engineer (SRE), and Cloud Infrastructure Engineer are direct career progressions and often command premium compensation at product-based companies and hyperscalers (AWS, Google, Microsoft).

The 2026 DevOps Engineer Roadmap

The following roadmap is organized by skill domain, progressing from foundational knowledge to advanced specialization. Focus on mastering each area before moving to the next.

1. Linux Administration & Shell Scripting

Linux is the operating system underpinning virtually every production server, container, and cloud workload. Solid Linux proficiency is non-negotiable for a DevOps engineer.

Core areas to master:

File system hierarchy, permissions, and user management
Process management (systemd, journalctl, cron)
Networking fundamentals: TCP/IP, DNS, SSH, firewall rules (iptables/nftables)
Package management (apt, yum/dnf)
Bash scripting: automation, parsing logs, cron jobs, error handling

Recommended Resources:

Book: UNIX — Concepts and Applications by Sumitabha Das
KodeKloud Engineer — hands-on Linux labs with real-world scenarios
YouTube: "Get Ahead with msdeep" (Linux & networking fundamentals)
GitHub: github.com/msdeep14/getAheadWithMe — curated Linux, networking, and OS concepts

2. Scripting & Programming

Automation is the cornerstone of DevOps. Proficiency in at least one scripting language is essential for writing deployment scripts, automation tools, and infrastructure utilities.

Recommended languages:

Bash — for system-level automation and CI/CD pipeline scripts
Python — for complex automation, API integrations, and tooling (preferred at most organizations)
Go — increasingly used for writing cloud-native tools and Kubernetes controllers

Recommended Resources:

Udemy: Complete Bash Shell Scripting
Udemy: Python Scripting for Automation
For FAANG/Tier-1 companies, supplement with Python-based DSA preparation

3. Networking Fundamentals

Understanding networking is critical for debugging connectivity issues in Kubernetes clusters, configuring load balancers, securing cloud VPCs, and optimizing service mesh configurations.

Key concepts:

OSI model and TCP/IP stack
DNS resolution, HTTP/HTTPS, TLS/SSL
Load balancing strategies (round-robin, least-connections, IP hash)
VPC design, subnets, security groups, NACLs
Service mesh fundamentals (Istio, Linkerd)
Common tools: curl, netstat, tcpdump, nmap, dig, traceroute

4. CI/CD Pipelines — Jenkins, GitHub Actions, GitLab CI

Continuous Integration and Continuous Delivery are the operational backbone of modern software delivery. A DevOps engineer must be capable of designing, maintaining, and troubleshooting production-grade pipelines.

Core competencies:

Designing multi-stage CI/CD pipelines (build, test, security scan, deploy)
Branching strategies: GitFlow and trunk-based development
Integration with artifact registries (JFrog, Nexus, ECR)
Pipeline security: secret management, SAST/DAST integration
Jenkins: declarative pipelines, shared libraries, Jenkins agents on Kubernetes
GitHub Actions: workflow syntax, reusable workflows, self-hosted runners

Recommended Resources:

Udemy: CI/CD with Jenkins Pipeline, Ansible, Kubernetes & Docker — end-to-end pipeline course with hands-on deployments
KodeKloud: Jenkins and GitHub Actions labs

5. Containers — Docker & Kubernetes

Container orchestration is the most sought-after skill in the DevOps landscape. Kubernetes has become the de facto standard for running production workloads at scale.

Docker fundamentals:

Image layering, multi-stage builds, and optimization
Docker Compose for local development environments
Container security: non-root users, read-only file systems, image scanning

Kubernetes core areas:

Workloads: Deployments, StatefulSets, DaemonSets, Jobs, CronJobs
Networking: Services, Ingress controllers, NetworkPolicies, DNS
Storage: PersistentVolumes, StorageClasses, CSI drivers
Configuration management: ConfigMaps, Secrets, Kustomize, Helm
Autoscaling: HPA, VPA, KEDA, Karpenter vs. Cluster Autoscaler
Observability: Prometheus, Grafana, Loki, OpenTelemetry
Security: RBAC, PodSecurity, OPA Gatekeeper, Falco

Recommended Resources:

KodeKloud: Docker and Kubernetes for Absolute Beginners, CKA/CKAD courses
KodeKloud Engineer — real-world Kubernetes tasks
Official Kubernetes documentation: kubernetes.io/docs

6. Infrastructure as Code — Terraform

Infrastructure as Code (IaC) enables teams to provision, version, and manage cloud infrastructure with the same rigor applied to application code. Terraform is the industry-standard tool for multi-cloud IaC.

Key skills:

Terraform core workflow: init, plan, apply, destroy
Writing reusable modules and remote state management
Provider configuration: AWS, GCP, Azure
State locking with S3 + DynamoDB or Terraform Cloud
Integration with CI/CD pipelines (GitHub Actions or Jenkins)
Policy as Code: Sentinel, OPA

Recommended Resources:

Udemy: Terraform — Beginner to Advanced
HashiCorp Learn: developer.hashicorp.com/terraform/tutorials

7. Cloud Platforms — AWS, GCP, Azure

Cloud proficiency is a baseline expectation for senior DevOps and Platform Engineering roles. Focus on one cloud provider for depth before expanding to multi-cloud.

Core services to master (AWS as reference):

Compute: EC2, ECS/Fargate, EKS, Lambda
Networking: VPC, Route53, CloudFront, ALB/NLB
Storage: S3, EBS, EFS
Security: IAM, KMS, Secrets Manager, GuardDuty
Observability: CloudWatch, CloudTrail, X-Ray

8. Configuration Management — Ansible

Configuration management tools ensure consistency across server fleets and reduce configuration drift. Ansible's agentless architecture and YAML-based playbooks make it the most accessible entry point.

Key skills:

Writing idempotent playbooks and roles
Inventory management: static and dynamic inventories
Ansible Vault for secrets management
Integration with Terraform for full provisioning workflows

Certifications That Accelerate Your Career

Certifications validate your skills and significantly improve your visibility in competitive job markets:

CKA — Certified Kubernetes Administrator (CNCF) — highly valued for Kubernetes-heavy roles
CKAD — Certified Kubernetes Application Developer (CNCF)
Terraform Associate (HashiCorp) — widely recognized for IaC proficiency
AWS Solutions Architect Associate / Professional
GCP Professional Cloud DevOps Engineer
Red Hat Certified System Administrator (RHCSA) — strong Linux credential

Recommended Books

The Phoenix Project — Gene Kim et al.
The DevOps Handbook — Gene Kim et al.
Site Reliability Engineering — Google SRE team
Kubernetes in Action — Marko Luksa
Terraform: Up & Running — Yevgeniy Brikman

Pro Tips

Build a hands-on home lab. Deploy a local Kubernetes cluster with Minikube or Kind and a full observability stack (Prometheus + Grafana + Loki).
Contribute to open source. Contributing to CNCF projects like Argo CD or cert-manager builds real-world credibility.
Document your work. Maintain a technical blog or GitHub portfolio showcasing projects, runbooks, and architectural decisions.
Target product-based companies strategically. Supplement DevOps skills with DSA preparation (Python) and system design for FAANG/Tier-1 roles.
Scaler DevOps Bootcamp: scaler.com
Learn.CodeAndDebug: learn.codeanddebug.in

Final Thoughts

The DevOps engineering discipline continues to evolve rapidly — with Platform Engineering, GitOps, FinOps, and AI-assisted operations emerging as frontier areas in 2025-2026. The foundational skills in this roadmap remain relevant regardless of these trends; mastery of Linux, containers, IaC, and CI/CD provides the technical bedrock for all specializations.

Stay consistent, prioritize hands-on practice, and engage with the DevOps community through KodeKloud, CNCF Slack, and local meetups.

For questions or feedback: LinkedIn — Tripura Kant

Related Posts:
Top Interview Questions for DevOps Roles

TechZonein

How I Passed the Certified Kubernetes Administrator (CKA) Exam: My Experience

Exam Overview

Study Tips: Here are some tips that helped me prepare for the CKA exam:

Links

Exam Advice: Here is some advice for taking the CKA exam:

The 3-Week Schedule for AWS Certified CloudOps Engineer - Associate

The 3-Week Schedule for AWS Certified CloudOps Engineer - Associate

Week 1 — Monitoring and Reliability (44% of the exam)

Week 2 — Automation, Security, Networking (56%)

Week 3 — Mocks and repair

How to Fix Kubernetes CrashLoopBackOff

1) What is CrashLoopBackOff in Kubernetes?

2) Quick Commands to Debug CrashLoopBackOff

Step 1: Check pod status

Step 2: Describe the pod (most important)

Step 3: Check logs

Step 4: Check container exit code

3) Fix #1: Application is Crashing on Startup

Symptoms

Fix

4) Fix #2: Wrong Command or Entrypoint

Symptoms

Check your deployment YAML

Fix

5) Fix #3: Missing Environment Variables / Secrets

Symptoms

Check env vars

Fix

6) Fix #4: Liveness Probe Killing the Pod

Symptoms

Check pod events

Fix

7) Fix #5: Image Issues or Wrong Architecture

Symptoms

Fix

8) Fix #6: OOMKilled (Memory Limit Exceeded)

Symptoms

Check pod status

Fix

9) Fix #7: Volume Mount or Permission Issues

Symptoms

Check volume mount in YAML

Fix

10) Best Practices to Prevent CrashLoopBackOff

11) FAQ

Q1. How long does CrashLoopBackOff last?

Q2. How do I stop CrashLoopBackOff immediately?

Q3. What is the fastest way to debug?

Conclusion

Kubernetes for Begineers [PDF Notes]

ZOOM Referall form

10 Books Every DevOps Engineer Should Read

Cloud & DevOPS Material

Kubernetes Workloads (Deployments, Jobs, CronJobs, etc.)

Deployments

Creating a Deployment

Statefulset

Example

Daemonsets

Features of Daemonsets:

Example

Cron Jobs

Example

Jobs

Example

Linux Services KodeKloud Engineer Task Success

Karpenter Vs Kubernetes Cluster Autoscaler

DevOps Series-Day2- Become Linux Pro

DevOps Series-Day1-DevOps Intro

Could not resolve host : Digital ocean Droplet issue resolved

unable to resolve host Digital Ocean

Top Interview Questions for DevOps Role in 2022

How to Become DevOps Engineer| Roadmap for DevOps Engineer[2026] Updated

What is DevOps Engineering?

Why DevOps? Market Demand & Salary Outlook

The 2026 DevOps Engineer Roadmap

1. Linux Administration & Shell Scripting

2. Scripting & Programming

3. Networking Fundamentals