DevOps Tools Engineer (Exam 701-200)

For IT leaders: Many teams still move code to production through manual handoffs, inconsistent environments, and ad hoc fixes. That slows releases and makes outages harder to diagnose. This course gives your team a shared, end-to-end DevOps workflow: Git and GitHub for source control and collaboration, Docker for container workflows, Kubernetes for orchestration, and Jenkins for CI/CD pipelines.

Role fit: This course is best for developers, system administrators, and DevOps engineers moving into pipeline-driven delivery. It works well for a whole team because everyone learns the same workflow, from first commit to a container deployed on Kubernetes.

Time investment: Plan for about 13.7 hours across 16 skills per learner. Most teams pace it over several weeks alongside project work.

Change-management notes: The instructor is candid that Jenkins and Kubernetes are each large enough to support full careers. Expect working foundations and a prototype pipeline, not mastery. The course also frames CI/CD as a workflow that starts with source control events and moves through automated build, test, and deployment stages, which helps set realistic expectations before your team takes on internal rollouts. Lessons include modern AI-assisted development workflows and where Jenkins fits in the DevOps toolchain.

The ordered playlist structure supports a consistent learning path for the team and prepares learners for the DevOps Tools Engineer (701-200) exam.

IT teams complete this training to move code from a laptop to a running application through a repeatable pipeline, without relying on tribal knowledge or outside contractors. The course follows real build-outs rather than slideware. Learners version a project in Git, collaborate through GitHub, and package workloads in containers. They then use tools like Jenkins to automate build, test, and deployment workflows.

The instruction is candid about what first-time CI/CD setup looks like. Learners see how source control events kick off automation, how tools like Jenkins fit into the workflow, and how containers and Kubernetes support consistent delivery from development through production. These are the kinds of foundations that keep pipeline projects from stalling in production environments.

• Fewer "works on my machine" failures: Teams use containers to keep developer and production environments consistent. They configure ports, storage, and environment variables, and use container tooling to inspect and troubleshoot running workloads.
• Safer, independent releases: Practitioners learn how microservices and Kubernetes let teams scale and ship one service without disrupting others.
• Clearer pipeline troubleshooting: Teams connect code changes to build, test, and deployment events, making it easier to identify where a delivery workflow stops and what needs attention.
• Stronger cloud and container fluency: Team Leads gain a shared model for how cloud platforms, Docker, Kubernetes, and CI/CD automation fit into modern application delivery.

The course also addresses AI-assisted workflows, so teams understand where AI can support DevOps work without replacing the need for hands-on knowledge of cloud, containers, and Kubernetes.

Knowledge

• How microservices, containers, and Kubernetes fit together so applications run the same on a developer laptop as in production and scale service by service
• Where IaaS and PaaS fit, and how cloud platform storage, data, and compute services support application delivery
• How Git and GitHub act as the source of truth for the code base and the starting point for CI/CD workflows, including how repository changes can trigger tools like Jenkins
• Why multi-container applications need orchestration, and where Docker, Docker Compose, MicroK8s, and Kubernetes fit in the delivery life cycle
• The end-to-end security footprint of a cloud-based application, from the end user through every component that must be protected
• How build, test, deployment, and system feedback help teams troubleshoot pipeline and delivery failures

Ability

• Initialize repositories, commit and review history, and use branches to build features and fixes in parallel without destabilizing main code
• Pull, run, configure, and inspect Docker containers, including ports, storage, environment variables, and interactive shell access with docker exec
• Stand up a working MicroK8s deployment and create or manage Kubernetes resources with kubectl
• Install and secure Jenkins, including users, roles, and matrix-based authorization so GitHub can trigger builds, and troubleshoot failures from the system log
• Build a Jenkins pipeline with build and test stages and work through common first-run dependency issues
• Deliver continuously by having Jenkins push Docker images to Docker Hub and deploy them to Kubernetes, using AI assistants to speed up complex setup