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Candidate 14 || Ultimate Excellent Senior DevOps Engineer Real Interview For 3 to 8 yrs || Ireland
Manish Tiwari
Overview
This video summarizes a senior DevOps engineer interview focusing on a candidate with 3-8 years of experience. The interview covers a wide range of DevOps concepts and tools, including CI/CD pipelines with Jenkins, cloud services (AWS, Azure), containerization (Docker, Kubernetes), infrastructure as code (Terraform), and system monitoring. The candidate demonstrates knowledge of deployment strategies, branching, versioning, high availability, scalability, networking, and troubleshooting common issues in Linux servers and cloud environments. The discussion also touches upon best practices for managing sensitive information, secrets, and state files across different environments.
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Chapters
- Candidate introduces themselves as a Cloud and DevOps engineer with 3 years of experience in AWS and Azure, skilled in Jenkins, Docker, and Kubernetes.
- Experience includes provisioning infrastructure, managing CI/CD pipelines, and recently completing a Master's in Cloud Computing.
- Starts the day by checking emails and Slack, then prioritizes Jira tickets, addresses business impact issues, and works on assigned tasks, including providing Root Cause Analyses (RCAs).
- Possesses Azure AZ-900 and AWS Solutions Architect Associate certifications, and is preparing for CK certifications.
Understanding a candidate's background and daily routine provides context for their experience and how they approach their work.
Candidate's daily routine involves checking Jira tickets and addressing business impact issues.
- Continuous Delivery (CD) involves deploying code to production after automated testing and feedback, while Continuous Deployment (also CD) automatically deploys every validated change to production.
- Jenkins is used for managing CI/CD pipelines, handling sensitive information like tokens and credentials securely through Jenkins credentials plugins.
- Jenkins pipelines typically include stages for code cloning, testing, static code analysis, building artifacts, creating Docker images, and deploying to Kubernetes (EKS).
- Branching strategies like feature, dev, and main branches are used, with code versioning managed via tags.
CI/CD is a core DevOps practice; understanding its nuances and how tools like Jenkins implement it is crucial for efficient software delivery.
A Jenkins pipeline stage involves cloning from a repository, followed by testing, static code analysis, building an artifact, creating a Docker image, and deploying to Kubernetes.
- Hotfix deployments involve creating a dedicated hotfix branch to address critical bugs in production.
- The fix is developed on the hotfix branch, then merged into the main branch.
- Git history and merge logs are used to track changes and differentiate between code versions, even after multiple updates.
- While tags are used for versioning, the candidate was unsure about creating tags directly from branches.
Effective strategies for handling urgent fixes and maintaining clear version history are essential for stable production environments.
To fix a bug in version 10.0, a new 'hotfix' branch is created, the code is fixed there, and then merged back into the main branch.
- Key AWS services used include EC2, S3, ECR, EKS, and SNS.
- For migrating data (e.g., customer images) between S3 buckets in different AWS accounts, cross-region replication can be utilized.
- S3 bucket lifecycle policies can be used to archive data for long-term storage.
- When a customer agreement ends, data stored in their dedicated S3 bucket can be moved to a shared account's S3 bucket using replication policies.
Understanding how to leverage cloud services for data management, migration, and archival is vital for cost-efficiency and compliance.
Moving customer images from one S3 bucket in Account A to another S3 bucket in Account B using cross-region replication.
- Achieving high availability and scalability in AWS involves using load balancers, deploying across multiple Availability Zones (AZs), and configuring Auto Scaling groups.
- Auto Scaling groups adjust the number of servers based on traffic and custom metrics (CPU, memory, network I/O) to maintain performance.
- Scaling policies include Step Scaling, Target Tracking, and Scheduled Actions.
- A scenario involving conflicting scaling policies (scheduled vs. target tracking) highlights the need to understand how load and predefined schedules interact.
Ensuring applications can handle varying loads and remain accessible is fundamental to providing a reliable user experience.
Using Auto Scaling to automatically increase the number of EC2 instances when CPU utilization exceeds a defined threshold.
- A request to a DNS name (e.g., manishdevops.com) first resolves via DNS (Route 53) to a load balancer (ALB).
- The load balancer forwards traffic to an Ingress Controller, which uses path-based routing to direct requests to specific Kubernetes Services.
- Services then route traffic to the appropriate Pods within the cluster.
- Troubleshooting unhealthy target groups involves checking health probes, system logs, and performance metrics on the target servers.
Understanding the request flow from DNS to Pods in Kubernetes is essential for debugging connectivity and performance issues.
A request to 'manishdevops.com' is resolved by Route 53 to an ALB, then routed by the Ingress Controller to a specific service, and finally to a Pod.
- Containers are lightweight because they share the host OS kernel, unlike traditional VMs that require a separate guest OS.
- You cannot delete a Docker image while a container using it is running; a force delete is possible but will break the running container.
- Docker networking modes include bridge, host, and overlay networks.
- Kubernetes is a container orchestrator used to manage the deployment, scaling, and lifecycle of multiple containers.
- Network policies in Kubernetes can restrict communication between pods in different namespaces.
- For sensitive data like certificates, AWS Secrets Manager is preferred over Kubernetes Secrets or ConfigMaps due to its rotation capabilities.
Containerization and orchestration are fundamental to modern application deployment, enabling efficiency, scalability, and portability.
Containers are considered lightweight because they utilize the host operating system's kernel, reducing resource overhead compared to virtual machines.
- When deploying applications, choose EC2 for monolithic architectures and Kubernetes for microservices.
- Kubernetes is used to manage multiple containers, orchestrate their lifecycles, and monitor their interactions.
- To ensure specific pods (e.g., login page) run on particular node types (e.g., C5.xlarge), node affinity rules are used.
- Terraform's `for_each` or `count` blocks can be used to deploy multiple resources with similar configurations.
- Terraform workspaces or separate directories are used to manage state files for different environments (e.g., pre-prod, staging, production) to prevent overwrites.
Infrastructure as Code (IaC) and proper state management are critical for consistent, repeatable, and scalable infrastructure deployments.
Using Terraform's `for_each` block to provision five EC2 instances of the same type in a specific AWS region.
- Troubleshooting a slow Linux server involves checking load average (using `top`), identifying high-CPU processes (using `ps aux --sort=-%cpu`), and checking disk space (`df -h`).
- SSH uses secure shell protocol with key-based authentication (private key exchange) for secure remote connections.
- Recent learning includes Argo CD for continuous delivery, multi-stage Docker builds, and monitoring with Prometheus and Grafana.
- The candidate learned about automating deployments with Argo CD, where code changes trigger updates in the artifact repository, which Argo CD then deploys to Kubernetes.
Effective troubleshooting skills and a commitment to continuous learning are essential for adapting to the evolving DevOps landscape.
To diagnose a slow Linux server, the candidate would first check the load average using the `top` command.
Key takeaways
- CI/CD pipelines automate the software delivery process, reducing manual effort and increasing deployment frequency.
- Securely managing sensitive information like credentials and tokens is paramount in CI/CD and cloud environments.
- Understanding different branching and versioning strategies is crucial for effective code management and rollback capabilities.
- Leveraging cloud services like AWS S3 and EKS requires knowledge of their specific features for data management, scalability, and availability.
- Auto Scaling and load balancing are key components for building highly available and scalable applications in the cloud.
- Kubernetes provides powerful orchestration capabilities for managing containerized applications at scale.
- Infrastructure as Code tools like Terraform enable consistent and repeatable infrastructure deployments across different environments.
- Continuous learning and adapting to new tools like Argo CD are vital for staying current in the DevOps field.
Key terms
CI/CDJenkinsContinuous DeliveryContinuous DeploymentDockerKubernetesEKSAWS S3Auto ScalingLoad BalancerIngress ControllerRoute 53TerraformArgo CDSecrets ManagerConfigMapNode AffinityNetwork Policies
Test your understanding
- What is the fundamental difference between Continuous Delivery and Continuous Deployment?
- How can sensitive information like API keys be securely managed within a Jenkins pipeline?
- Explain the process of a hotfix deployment and how it differs from a regular code merge.
- How would you ensure an application remains highly available and scalable in AWS?
- Describe the typical request flow from a user accessing a website via DNS to a pod running in a Kubernetes cluster.
- Why are containers considered lightweight compared to virtual machines?
- What strategies can be employed in Terraform to manage state files for multiple distinct environments like staging and production?