DevOps Interview Questions

Situation: At my current organisation, we needed to migrate a legacy monolithic application to a cloud-native microservices architecture to improve scalability and performance.

Task: My role was to design and implement the cloud infrastructure using AWS and Kubernetes while ensuring zero downtime during the transition.

Action: I led a team in containerizing the application using Docker, set up Kubernetes clusters using EKS, and automated the deployment process using Terraform and CI/CD pipelines. We also optimised database performance by introducing read replicas and auto-scaling policies.

Result: The migration was successfully completed with zero downtime. Performance improved by 40%, and deployment time was reduced from several hours to a few minutes, enhancing the overall development workflow.

I primarily work with AWS, where I manage services like EC2, S3, RDS, ECS, EKS, Lambda, and IAM. I also have experience with Azure and Google Cloud, particularly in setting up VMs, Kubernetes clusters, and CI/CD integrations.

Yes, I have obtained the following certifications:

• AWS Certified Solutions Architect – Associate
• AWS Certified DevOps Engineer – Professional
• Terraform Associate Certification

These certifications validate my expertise in cloud architecture, security, and automation. Additionally, I have completed formal training in Kubernetes administration and DevOps practices.

Yes, I have worked extensively with both ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) for managing containerized workloads. I have designed and deployed highly available, auto-scaling microservices using these services, optimising performance and reducing infrastructure costs.

Situation: Our organisation required a container orchestration solution for its growing microservices architecture.

Task: My role was to evaluate and implement the best fit between ECS and EKS for different workloads.

Action:

• For ECS, I deployed containerized applications using AWS Fargate, setting up auto-scaling and monitoring using CloudWatch and AWS App Mesh.
• For EKS, I set up Kubernetes clusters, configured Helm charts, managed networking with VPC CNI, and implemented observability using Prometheus and Grafana.

Result: The strategic use of both services improved deployment speed by 60%, reduced cloud costs, and ensured seamless scaling based on traffic spikes.

Yes, my organisation develops its own cloud-native applications.

Situation: A new product was being developed, requiring scalable and secure infrastructure.
Task: My role was to ensure reliable cloud infrastructure, automate deployments, and integrate security best practices.
Action: I set up CI/CD pipelines using Jenkins and GitHub Actions, implemented infrastructure as code with Terraform, and optimised application performance using AWS Auto Scaling and caching mechanisms.
Result: The project was delivered ahead of schedule, and infrastructure reliability improved, reducing downtime by 30%.

I have done both.

Situation: Our company needed a custom logging and monitoring system for our cloud applications.
Task: I was responsible for designing and implementing a new solution using AWS services.
Action: I developed a serverless logging pipeline using AWS Lambda, CloudWatch, and S3, ensuring efficient log storage and analysis.
Result: The solution reduced log processing time by 50% and improved issue resolution efficiency.

An Auto Scaling Group (ASG) in AWS automatically scales EC2 instances based on predefined metrics.

Setup Includes:

• Launch template with instance configuration
• Minimum, maximum, and desired instance counts
• Scaling policies based on metrics like CPU utilization, request rate, or memory usage

Example Situation: Our application faced high traffic spikes during peak hours.
Action: I configured an ASG with CPU utilization thresholds and enabled predictive scaling.
Result: Application uptime improved, handling 30% more traffic without manual intervention.

Yes, I have extensive experience using Jenkins for CI/CD automation, pipeline scripting, and integration with cloud environments.

Example: I automated application deployments using Jenkins, reducing release cycles from hours to minutes.

I am highly experienced with Terraform.ituation: We needed a repeatable and scalable way to deploy AWS infrastructure.
Action: I developed modular Terraform code, set up remote state management, and integrated it with CI/CD.
Result: Infrastructure provisioning time was cut by 70%, improving agility.

I have advanced experience in Docker, including:

• Writing efficient Dockerfiles
• Managing multi-container applications with Docker Compose

Deploying containerized workloads in ECS and EKS

Situation: Migrating a monolithic application to a microservices architecture on AWS.
Task: Ensure seamless migration without downtime.
Action:

• Decomposed the monolith into independent services using Docker & Kubernetes

Implemented API Gateway, IAM roles, and observability with Prometheus
Result: Improved application scalability by 50%, with zero downtime.

Distributed tracing tracks requests across microservices to diagnose performance bottlenecks.

Example: I implemented AWS X-Ray and OpenTelemetry to monitor service latency, reducing troubleshooting time by 40%.

Yes, I led a project to automate cloud deployments using Terraform and CI/CD.

Situation: Manual infrastructure provisioning caused delays.
Task: Automate deployments to improve efficiency.
Action: Developed Terraform modules, integrated with Jenkins, and implemented security best practices.
Result: Deployment time reduced from days to minutes, and security posture improved.

I am a cloud computing and DevOps professional with expertise in AWS, Kubernetes, Terraform, and CI/CD automation. I have experience in designing, deploying, and maintaining scalable, highly available cloud infrastructures. My technical skills include container orchestration, infrastructure as code, automation, and cloud security.

Over the years, I have worked on cloud migration, containerized applications, and automation projects, optimising performance and reducing operational overhead.

Situation: My team was tasked with migrating a monolithic application to the cloud for better scalability and performance.
Task: I was responsible for architecting the cloud infrastructure, implementing Kubernetes (EKS), and ensuring zero downtime during migration.
Action: I containerized the application using Docker, deployed it to EKS, and set up Terraform to automate infrastructure provisioning.
Result: The migration improved deployment speed by 60%, reduced cloud costs, and enhanced reliability.

I primarily work with AWS, but I also have experience with Azure and Google Cloud. In AWS, I manage EC2, S3, RDS, EKS, Lambda, IAM, and networking services.

Yes, I hold the following certifications:
✅ AWS Certified Solutions Architect – Associate
✅ AWS Certified DevOps Engineer – Professional
✅ Terraform Associate Certification

I have also completed AWS training in Kubernetes, serverless computing, and DevOps methodologies.

Yes, I have used ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) to deploy and manage containerized applications.

Situation: Our company required container orchestration to improve scalability.
Task: I evaluated ECS vs. EKS and implemented the best fit for each use case.
Action:
✔ Used ECS for lightweight containerized workloads with AWS Fargate.
✔ Used EKS for Kubernetes workloads, deploying Helm charts, managing networking (VPC CNI), and setting up observability tools (Prometheus, Grafana).
Result: The solution provided seamless scaling, reduced latency, and cost savings.

Yes, we build our own cloud-native applications.

Situation: A new microservices-based product needed cloud infrastructure and security.
Task: I was responsible for setting up CI/CD pipelines, Kubernetes clusters, and cloud security.
Action: Implemented Jenkins pipelines, Terraform for infrastructure as code, and AWS IAM roles for security.
Result: The application launched successfully with a 99.99% uptime guarantee.

I have done both.

Example: I built a serverless log analytics system using AWS Lambda, S3, and CloudWatch, reducing log retrieval time by 50%.

An Auto Scaling Group (ASG) automatically scales EC2 instances based on CloudWatch metrics like:
✔ CPU utilization
✔ Network traffic
✔ Request per second (RPS)

Example: I configured ASG for an application, reducing costs by 30% during low-traffic hours.

Yes, I have extensive experience using Jenkins for CI/CD automation. I have:
✔ Automated builds and deployments
✔ Integrated it with Kubernetes and Terraform
✔ Improved deployment time from hours to minutes

I am highly proficient with Terraform.

Example: I built a multi-region AWS environment with Terraform, reducing deployment errors by 80%.

I have advanced experience in Docker, including:
✔ Writing optimized Dockerfiles
✔ Managing multi-container applications
✔ Deploying to EKS & ECS

🔹 Infrastructure automation (Terraform, Ansible)
🔹 CI/CD pipeline management (Jenkins, GitHub Actions)
🔹 Container orchestration (EKS, ECS)
🔹 Monitoring & troubleshooting (CloudWatch, Prometheus)

I have basic PHP knowledge and have supported Laravel application upgrades by:
✔ Updating dependencies
✔ Optimising database queries
✔ Debugging issues in Nginx and PHP-FPM logs

Yes, I have worked with:
✔ Relational Databases: MySQL, PostgreSQL, SQL Server
✔ NoSQL Databases: DynamoDB, MongoDB

1️ Choose RDS MySQL or EC2 self-hosted MySQL
2️ Configure VPC, security groups, and IAM roles
3️ Set up backups, multi-AZ replication, and monitoring

📊 CPU, memory, and IOPS usage
📊 Query response time
📊 Read/write latency

An Availability Zone (AZ) is an isolated AWS data center that improves fault tolerance.

Yes, I troubleshoot:
🔹 Network issues (VPC, Security Groups)
🔹 IAM permission errors
🔹 Database performance bottlenecks

Situation: Migrating a legacy app to AWS microservices.
Task: Ensure zero downtime & high scalability.
Action: Used EKS, Terraform, and CI/CD automation.
Result: Reduced deployment time by 80%.

Yes, I designed and implemented Terraform-based AWS infrastructure, reducing provisioning time from days to minutes.

Situation: I have worked extensively in both on-premises Linux environments and cloud-based Linux instances (AWS EC2, GCP, Azure VMs).
Task: Manage system administration, automation, and troubleshooting on Linux-based systems.
Action: Set up automated scripts for server monitoring, managed system patching, and optimized performance tuning.
Result: Improved system uptime, reduced manual intervention, and enhanced security posture.

You can use grep:

bash

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grep "YourName" filename.txt

To search recursively in all files within a directory:

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grep -r "YourName" /path/to/directory

To check disk utilization:

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df -h

To check folder/file sizes:

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du -sh *

df (Disk Filesystem): Shows available disk space for file systems.
du (Disk Usage): Shows how much space directories/files are using.

Example:

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df -h  # Shows disk usage for file systems

du -sh /home/user  # Shows space used by /home/user

Situation: Our security team identified vulnerabilities in production Linux servers.
Task: Apply security patches without downtime.
Action: Used Yum (CentOS), APT (Ubuntu), and AWS SSM to automate patches.
Result: Patched all critical systems with zero downtime and improved security compliance.

Yes, I have worked with:
✔ Middleware: Apache Tomcat, Nginx, HAProxy
✔ Databases: MySQL, PostgreSQL, MongoDB, DynamoDB

Situation: A client needed a high-performance Java application running on JBoss.
Task: Set up, configure, and optimize JBoss for scalability and security.
Action: Tuned heap memory, configured connection pools, and set up reverse proxy with Nginx.
Result: Improved response time by 40% and reduced server crashes.

✔ Dockerizing applications and deploying in Kubernetes (EKS, GKE, AKS)
✔ Migrating legacy applications to containers
✔ Implementing multi-container microservices with Docker Compose

Situation: We needed to deploy a Python API as a container.
Task: Create a lightweight, secure container image.
Action:

Write Dockerfile:

Dockerfile

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FROM python:3.9

WORKDIR /app

COPY . .

RUN pip install -r requirements.txt

CMD ["python", "app.py"]

Build & Run Container:

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docker build -t myapp .

docker run -d -p 5000:5000 myapp

Result: The application was containerized, making it scalable and portable.

✔ Public Subnet: Has direct internet access via Internet Gateway (IGW).
✔ Private Subnet: No direct internet access; uses NAT Gateway for outgoing traffic.
✔ Isolated Subnet: No inbound or outbound internet access (used for security).

Yes, I have experience managing Amazon RDS for MySQL, PostgreSQL, and SQL Server, including:
✔ Automating backups & snapshots
✔ Scaling instances dynamically
✔ Configuring multi-AZ deployments

✔ Single AZ: Runs in one availability zone (AZ)—if that AZ fails, the database is unavailable.
✔ Multi AZ: RDS automatically replicates data to a standby instance in another AZ.
✔ Failover: If the primary instance fails, AWS RDS automatically switches to the standby instance.

AWS RDS failover typically happens within 60–120 seconds.

Use the following command:

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java -version

To check Java environment variables:

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echo $JAVA_HOME

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du -sh */

This will display the size of each subdirectory in the current directory.

Use the sed command:

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sed -i 's/TEXT/TEXT1/g' X

This will replace TEXT with TEXT1 inside file X.

HashiCorp Vault is a secrets management tool used for storing API keys, passwords, and certificates securely.

Experience:
✔ Used Vault for AWS IAM credentials management
✔ Implemented dynamic secrets for PostgreSQL databases
✔ Integrated Vault with Kubernetes for application secrets