Q1: Preventing accidental resource deletion?
lifecycle { prevent_destroy = true } terraform plan before apply Q2: Handling API rate limiting?
time_sleep between ops Q3: Recovering from a corrupted state file?
terraform import Q4: Backend migration?
terraform state pull → update backend → terraform init -migrate-state Q5: Handling state drift in prod?
terraform plan in CI/CD terraform importQ6: Secrets management?
sensitive = true Q7: Zero-downtime infra updates?
create_before_destroy Q8: Structuring modules for enterprise?
Q9: Large-scale refactoring strategy?
terraform state mv Q10: Terraform testing best practices?
tflint, checkov #Terraform #DevOps #InterviewReady #IaC
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𝗘𝘅𝗽 𝗥𝗮𝗻𝗴𝗲: 𝟯-𝟱 𝘆𝗿𝘀
These 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗯𝗮𝘀𝗲𝗱 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 covered a wide range of technical and behavioural topics. Sharing some key questions that faced during the interview:
1. How would you handle a situation where a deployment script fails mid-way? What rollback mechanisms would you implement?
2. Explain the difference between a blue-green deployment and a canary deployment. When would you use each?
3. What are some common challenges in CI/CD, and how have you solved them in your projects?
4. Explain how you would troubleshoot a pipeline error in Jenkins related to dependency mismatches.
5. How would you implement security in a CI/CD pipeline to ensure compliance and prevent vulnerabilities?
6. What are the differences between Jenkins and GitHub Actions, and when would you choose one over the other?
7. How would you use Ansible in a CI/CD pipeline to deploy applications to multiple environments?
8. Explain the role of playbooks and inventory files in Ansible for deployment automation.
9. How would you handle testing in a CI/CD pipeline to ensure changes don’t affect production?
10. What are the considerations for setting up a continuous deployment process in a cloud environment like AWS or Azure?
11. If CPU utilization on a server is consistently above 90%, what steps would you take to investigate and address the issue?
12. Imagine you need to migrate a high-performance computing (HPC) infrastructure to the cloud. How would you ensure a seamless transition while maintaining availability and performance?
13. Your team is experiencing increased latency in database queries due to high traffic. What steps would you take to diagnose and reduce this latency?
14. Suppose there’s a sudden surge in cloud costs. How would you go about identifying the cause and reducing unnecessary expenses?
15. Tell me how you previously reduced cloud expenditure by 20%.
16. You’ve been asked to design a logging and monitoring solution for a new application. What factors would you consider, and how would you prioritize them?
17. Imagine you are responsible for managing SSL/TLS certificates across multiple environments. How would you ensure they’re up to date and compliant with security standards?
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(and what you can expect during interviews)
• Linux & Shell Scripting
• Docker & K8s basics
• Basic Git workflows
• Foundational DevOps concepts (CI/CD | Virtualization/ Containerization etc)
• Troubleshooting skills (how can you fix a broken pipeline)
• Jenkins / GitLab CI basics
• AWS /GCP/ Azure core services
• Kubernetes for orchestration
• Terraform for infrastructure
• Ansible/Chef for configuration (in my previous life)
• Logging Stack (Datadog/ELK)
• Monitoring Stack (Datadpg/Prometheus/Grafana)
• Python/Go automation (end-to-end workflow)
• AWS/GCP/Azure advanced services
Same tools but with architectural focus..
• Infrastructure for scalability
• Security by design
• DevSecOps Implementation strategies
• Cloud Migrations ( understanding)
• Service mesh Implementation & Management
• Cost optimization patterns
• Cross-cloud solutions
• Platform engineering
The main takeaway..
Although preparation with in-demand tools is necessary, understanding system design patterns is more crucial than knowing every tool.
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DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning
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Defines how and where a Pod should run.
Tolerations – Allow pods to be scheduled on tainted nodes.
Security Context – Sets privileges, user/group IDs, etc.
Restart Policy – Controls if/how the pod restarts (Always, OnFailure, Never).
Node Selector – Specifies which node the pod can be scheduled on.
Affinity Rules – More advanced node/pod placement rules.
Handles the scheduling and management of pods.
Scheduler – Assigns pods to suitable nodes.
Kubelet – Node agent that manages pods and containers.
Controller – Manages desired state (e.g., ReplicaSet).
Node – Actual VM or server in the cluster.
Container Runtime – Software that runs containers (e.g., containerd, CRI-O).
CPU/Memory – Resources consumed by running containers.
Information needed to run a container within the pod.
Container Image – Blueprint to run the container.
Environment Variables – Config values injected into containers.
Ports – Defines container’s listening ports.
Probes:
Startup Probe – Checks if the app has started.
Readiness Probe – Checks if the app is ready to serve traffic.
Liveness Probe – Checks if the app is still alive or stuck.
Core building block of Kubernetes workloads.
Pod – Smallest deployable unit that can contain one or more containers.
Namespace – Isolates resources in the cluster.
Annotations – Attach non-identifying metadata to the pod.
IP Address – Each pod gets its own IP for communication.
Storage accessible by containers in a pod.
Secret – Stores sensitive data like passwords.
ConfigMap – Stores configuration data as key-value pairs.
EmptyDir – Temporary storage shared between containers.
PersistentVolumeClaim (PVC) – Connects to external storage for persistence.
The actual running applications.
Init Containers – Run before main containers, used for setup tasks.
App Containers – Main workload containers.
Main Container(s) – Core application logic runs here.
Helps with organization and selection.
Labels – Key-value pairs used to identify and group pods.
Selectors – Services or controllers use them to find pods.
Exposes pods and ensures stable networking.
Service – Abstracts pod IPs and provides a stable endpoint for access.
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Are you looking for high-quality DevOps and Cloud learning materials? Look no further! We’ve built a powerful Docs Portal where you can access 900+ valuable resources with just one click!
#DevOps #Cloud #Kubernetes #AWS #Terraform #CICD #DevOpsLearning #DocsPortal
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💡 Master a full-scale DevOps project from scratch!
This project is divided into two parts for better understanding and execution:
- MySQL (Database)
- Memcache (Caching)
- RabbitMQ (Message Broker)
- Tomcat (Application Server)
- Nginx (Web Server)
- Clone the repo
- Build & compile the application
- Deploy it across the infrastructure
🔥 A must-follow hands-on project for DevOps engineers, covering provisioning, deployment, and automation!
📣 Note: Fork this Repository🧑💻 for upcoming future projects, Every week releases new Project.
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- Learn to use the terminal: cd, ls, mkdir, rm, etc.
- Understand permissions, users, file systems.
- Practice on Ubuntu or CentOS (use a VM or cloud instance).
- Learn version control: git clone, commit, push, pull, branch, merge.
- Create your own project, push code to GitHub.
- Understand IP, DNS, HTTP/S, TCP/UDP, firewalls, ports.
- Tools: ping, curl, netstat.
- Start with Bash for automation (if, for, while, functions).
- OR learn Python for broader use and tool scripting.
- Learn to containerize applications.
- Concepts: Images, Containers, Volumes, Networks.
- Commands: docker build, run, ps, exec.
- Build pipelines: Code → Build → Test → Deploy
- Write .gitlab-ci.yml or Jenkinsfile
- Automate test & deployment steps.
- Understand Pods, Deployments, Services.
- Try minikube or kind for local practice.
- Learn kubectl commands and yaml configs.
- Package K8s apps using Helm
- Understand values.yaml and chart structure.
- Basics: EC2, S3, IAM, VPC (in AWS) or GCE, Cloud Storage, IAM (in GCP).
- Practice using free-tier.
- Learn to define infra in .tf files.
- terraform init, plan, apply, destroy.
- Manage infra across environments.
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blog.notharshhaa.site
DevOps & Cloud Space
Learn how AWS CodePipeline can automate your CI/CD workflow for faster, more reliable deployments. From source to production, streamline your release process with ease using AWS-native tools.
Speed up your deployments with AWS CodePipeline — a powerful CI/CD service that automates your entire release workflow, from code push to production.
- Automate builds, tests, and deployments
- Integrate with GitHub, CodeBuild, and CodeDeploy
- Deploy faster and more reliably on AWS
#DevOps #AWS #CodePipeline #CICD #Automation
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DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning
Explore the project:
Power up your DevOps workflow with automation, clarity, and full control!
#DevOps #Automation #ShellScript #PowerShell #OpenSource #SysAdmin
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(use cases included!)
Look for scenarios while prepping - examples below:
↳ Example: Legacy-to-Microservices Migration
↳ Key Achievement: Designed containerization strategy that reduced deployment time by 60%
↳ Example: Enterprise Data Lake Implementation
↳ Key Achievement: Optimized storage tiers saving $XXK/month while maintaining performance
↳ Example: Multi-tier Application Security
↳ Key Achievement: Implemented network segmentation reducing attack surface by X%
↳ Example: High-Availability Media Platform
↳ Key Achievement: Automated lifecycle policies cutting storage costs by X%
↳ Example: Monolithic-to-Cloud Database Migration
↳ Key Achievement: Zero-downtime migration of 5TB database
↳ Example: Enterprise IAM Implementation
↳ Key Achievement: Reduced security incidents by 80% through least-privilege access
↳ Example: Environment Automation with Terraform
↳ Key Achievement: Cut provisioning time from days to hours
↳ Example: Critical App Monitoring Setup
↳ Key Achievement: Reduced MTTR by XX% through proactive alerting
↳ Example: Event-Driven Processing Pipeline
↳ Key Achievement: Scaled to handle 1M+ daily events automatically
↳ Example: Microservices on Kubernetes
↳ Key Achievement: XX% availability across 20+ services
↳ Example: CI/CD Pipeline Implementation
↳ Key Achievement: Reduced release cycles from weeks to days
↳ Example: Resource Optimization
↳ Key Achievement: XX% cost reduction through right-sizing
This is a high-level map of core components of cloud, focused on DevOps - if you're focusing on other specializations - there'll be additional services/topics that you need to review as well.
- The technical challenge
- Your specific contribution
- Measurable business impact
- Lessons learned
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Break it into 4 layers
Individually there are 4 kubernetes components that you need to learn and implement.
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# Install AWS CLI
pip install awscli
# Configure AWS CLI
aws configure
# List IAM users
aws iam list-users
# Create IAM user
aws iam create-user --user-name <username>
# Attach policy to IAM user
aws iam attach-user-policy --user-name <username> --policy-arn arn:aws:iam::aws:policy/<policy-name>
# List all EC2 instances
aws ec2 describe-instances
# Start an EC2 instance
aws ec2 start-instances --instance-ids <instance-id>
# Stop an EC2 instance
aws ec2 stop-instances --instance-ids <instance-id>
# List all S3 buckets
aws s3 ls
# Upload file to S3 bucket
aws s3 cp <file-path> s3://<bucket-name>/<file-key>
# Download file from S3 bucket
aws s3 cp s3://<bucket-name>/<file-key> <file-path>
# List RDS instances
aws rds describe-db-instances
# Start RDS instance
aws rds start-db-instance --db-instance-identifier <instance-id>
# Stop RDS instance
aws rds stop-db-instance --db-instance-identifier <instance-id>
# List CloudWatch log groups
aws logs describe-log-groups
# Create CloudWatch log group
aws logs create-log-group --log-group-name <log-group-name>
# List Elastic Beanstalk environments
aws elasticbeanstalk describe-environments
# Update environment to new version
aws elasticbeanstalk update-environment --environment-name <env-name> --version-label <version-label>
# List CloudFormation stacks
aws cloudformation describe-stacks
# Create CloudFormation stack
aws cloudformation create-stack --stack-name <stack-name> --template-body file://<template-file>
# Update CloudFormation stack
aws cloudformation update-stack --stack-name <stack-name> --template-body file://<template-file>
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In a microservices architecture, 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 (𝗔𝗟𝗕) is often the go-to solution for routing incoming requests to the correct microservices based on their paths. But here's the key question: 𝘿𝙤 𝙮𝙤𝙪 𝙣𝙚𝙚𝙙 𝙖𝙣 𝘼𝙋𝙄 𝙂𝙖𝙩𝙚𝙬𝙖𝙮 𝙤𝙣 𝙩𝙤𝙥 𝙤𝙛 𝙖𝙣 𝘼𝙇𝘽?
The answer depends on how your microservice APIs are intended to be used:
If the APIs provided by the microservices are solely for internal use (within your VPC or Account), there’s no need for an additional API Gateway. The ALB’s DNS endpoint is sufficient to access the APIs directly.
If you’re exposing your microservices' APIs to external consumers (e.g., business partners, external apps), an API Gateway becomes essential. It provides:
While API Gateway offers these benefits, remember that it adds operational complexity and cost. 𝗜𝗳 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗶𝘁, 𝗮𝘃𝗼𝗶𝗱 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁 𝘂𝗻𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝗶𝗹𝘆.
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The working of static pods can be explained with the help of steps below:
Static pods are not created through the Kubernetes API server, so they do not have the full capabilities of API-managed pods. However, the kubelet creates a 𝐦𝐢𝐫𝐫𝐨𝐫 𝐩𝐨𝐝 in the API server for visibility, allowing tools like 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 to display their status.
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DevOps & Cloud (AWS, AZURE, GCP) Tech Free Learning
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1. You are running a containerized application that crashes intermittently without logging anything useful. How do you debug this behavior?
2. Your CI/CD pipeline pushes a new Docker image that fails only in production, not in staging. How would you isolate and resolve the discrepancy?
3. Your Docker image builds are inconsistent across developers’ machines. How would you ensure repeatable builds?
4. How would you securely inject secrets into a container without hardcoding them in Dockerfile or exposing them in environment variables?
5. A container using a volume is not syncing changes back to the host machine. How do you diagnose and resolve this?
6. You need to migrate your local Docker-based app to Kubernetes. What Docker-specific configurations might cause issues during the migration?
7. Your container uses a large base image and takes a long time to download in remote environments. What strategies can you apply to improve this?
8. You notice a container has exited with an OOMKilled (Out Of Memory) status. How do you investigate and prevent this?
9. How would you monitor file system usage and inode exhaustion in a running container?
10. Your team needs to run GPU-based containers on a shared host. How do you design a secure and performant setup?
11. You want to roll back to a previous container version but don't have the previous Dockerfile. How do you retrieve and use the old image?
12. You need to isolate a set of containers with custom firewall rules. How do you implement this using Docker’s networking capabilities?
13. A container exposes multiple ports, but some are not accessible externally. How do you verify and expose the correct ports?
14. How do you configure Docker for a multi-architecture build (e.g., building for x86 and ARM simultaneously)?
15. Your Dockerfile uses ADD to fetch remote URLs, but the builds fail due to SSL errors in CI. How do you debug and solve this?
16. You notice layers in your Docker image are not being cached during builds. What could be causing this?
17. You are required to enforce immutability for Docker containers in production. How would you approach this?
18. How would you implement a security scanning workflow integrated with your CI/CD process for Docker containers?
19. What would you do if Docker container logs are rotated too frequently and important logs are being lost?
20. You suspect your container image has been tampered with. How do you validate its authenticity?
21. How do you enforce policy controls such as image whitelisting in a Docker deployment?
22. A base image you use has been deprecated. How do you manage and migrate all dependent services with minimal downtime?
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𝟭.𝗴𝗶𝘁 𝗱𝗶𝗳𝗳: Show file differences not yet staged.
𝟮. 𝗴𝗶𝘁 𝗰𝗼𝗺𝗺𝗶𝘁 -m "commit message": Commit all tracked changes with a message.
𝟯. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘁𝘂𝘀: Show the state of your working directory.
𝟰. 𝗴𝗶𝘁 𝗮𝗱𝗱 𝗳𝗶𝗹𝗲_𝗽𝗮𝘁𝗵: Add file(s) to the staging area.
𝟱. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁 -𝗯 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Create and switch to a new branch.
𝟲. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Switch to an existing branch.
𝟳. 𝗴𝗶𝘁 𝗰𝗼𝗺𝗺𝗶𝘁 --𝗮𝗺𝗲𝗻𝗱: Modify the last commit.
𝟴. 𝗴𝗶𝘁 𝗽𝘂𝘀𝗵 𝗼𝗿𝗶𝗴𝗶𝗻 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Push a branch to a remote.
𝟵. 𝗴𝗶𝘁 𝗽𝘂𝗹𝗹: Fetch and merge remote changes.
𝟭𝟬. 𝗴𝗶𝘁 𝗿𝗲𝗯𝗮𝘀𝗲 -𝗶: Rebase interactively, rewrite commit history.
𝟭𝟭. 𝗴𝗶𝘁 𝗰𝗹𝗼𝗻𝗲: Create a local copy of a remote repo.
𝟭𝟮. 𝗴𝗶𝘁 𝗺𝗲𝗿𝗴𝗲: Merge branches together.
𝟭𝟯. 𝗴𝗶𝘁 𝗹𝗼𝗴-𝘀𝘁𝗮𝘁: Show commit logs with stats.
𝟭𝟰. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘀𝗵: Stash changes for later.
𝟭𝟱. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘀𝗵 𝗽𝗼𝗽: Apply and remove stashed changes.
𝟭𝟲. 𝗴𝗶𝘁 𝘀𝗵𝗼𝘄 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Show details about a commit.
𝟭𝟳. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁 𝗛𝗘𝗔𝗗~𝟭: Undo the last commit, preserving changes locally.
𝟭𝟴. 𝗴𝗶𝘁 𝗳𝗼𝗿𝗺𝗮𝘁-𝗽𝗮𝘁𝗰𝗵-𝟭 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Create a patch file for a specific commit.
𝟭𝟵. 𝗴𝗶𝘁 𝗮𝗽𝗽𝗹𝘆 𝗽𝗮𝘁𝗰𝗵_𝗳𝗶𝗹𝗲_𝗻𝗮𝗺𝗲: Apply changes from a patch file.
𝟮𝟬. 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵 -𝗗 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Delete a branch forcefully.
𝟮𝟭. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁: Undo commits by moving branch reference.
𝟮𝟮. 𝗴𝗶𝘁 𝗿𝗲𝘃𝗲𝗿𝘁: Undo commits by creating a new commit.
𝟮𝟯. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗿𝗿𝘆-𝗽𝗶𝗰𝗸 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Apply changes from a specific commit.
𝟮𝟰. 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵: Lists branches.
𝟮𝟱. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁 --𝗵𝗮𝗿𝗱: Resets everything to a previous commit, erasing all uncommitted changes.
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We curated a dedicated website showcasing real-time DevOps and Cloud projects — from beginner-friendly setups to advanced, production-grade pipelines. Whether you're just starting out or want to level up your DevOps skills, this site has hands-on projects that cover:
Perfect for self-learning, building your portfolio, and prepping for DevOps interviews — every project is clearly explained with architecture diagrams, tools used, and objectives.
Whether you're just starting out or looking to level up your DevOps journey, this is your one-stop hub for practical experience!
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