Cisco CCNP Implementing DevOps Solutions and Practices (300-910 DEVOPS) v1.1

Network teams are increasingly expected to ship changes the way software teams do: through pipelines, version-controlled configuration, and automated validation instead of manual CLI sessions pushed straight to production. This 300-910 DEVOPS course gives IT Directors a structured way to build that capability inside an existing network engineering team.

Role fit: Built for network engineers with three to five years of experience. It suits Team Leads and IT Practitioners who are moving toward automation, CI/CD, and infrastructure as code. Learners should be comfortable working on a Linux command line, since the course starts by setting up an Ubuntu development environment.

Time investment: Plan for about 21 hours across 24 skills of roughly 45–60 minutes each. That pace works as a weekly cadence alongside normal operations.

Risk and change management: The course reinforces safer change practices your organization can adopt:

• Testing network automation against a test environment before promoting it to production.
• Using snapshots and staged environments so teams can return to a known starting point when errors appear.
• Running Terraform plan to preview the net effect of a change before it is applied.
• Securing CI/CD pipelines and the software development life cycle.

The course also takes a realistic view of tooling. For example, Kubernetes adds complexity that may not suit a less mature engineering team. That helps leaders set realistic adoption expectations.

How CBT Nuggets supports the outcome: Validation challenges at the end of each skill reinforce practical DevOps scenarios tied to the course objectives, including automation, collaboration, cloud environments, containerization, and infrastructure as code. Use Playlists to sequence the content for a cohort, and Team Reporting to track progress toward Cisco Certified DevNet Professional certification.

IT teams complete this training to move network changes from manual, one-off work to tested, repeatable pipelines. Instead of pushing configurations straight to production, your team learns to build a simulated test network, automate changes there first, and promote them to production only after the necessary checks pass. That approach directly reduces the risk of change-related outages.

• Safer rollouts with canary deployments: Engineers practice targeting a limited subset of devices, such as a region or low-impact group, then monitoring the result and either promoting the change fleet-wide or rolling it back before business users feel it.
• Consistent infrastructure automation: Your team works with tools such as Ansible and Terraform, including Terraform examples that configure Cisco ACI through the public DevNet sandbox.
• Faster pipeline troubleshooting: Team members learn to diagnose CI/CD failures caused by network issues, error codes, and hard-to-spot syntax errors. They also use linting tools so Jenkins and GitLab pipelines stay stable.
• Visibility before incidents escalate: Using AppDynamics and Kubernetes monitoring, engineers bring application and infrastructure metrics into clearer view. They set threshold alerts and use historical data to explain traffic spikes.

Scenario-based validation challenges cover practical decisions as well. These include whether to move from Docker Swarm to Kubernetes given your team's maturity, and how to weigh multi-cloud availability against management complexity. Team Leads and IT Directors get practitioners who can justify these architecture choices, not just run the commands.

Knowledge

• How CI/CD pipelines are built in Jenkins and GitLab, including multi-stage pipelines, input/output streams, and error codes
• Deployment strategies such as canary releases: test changes on a limited subset of devices, monitor them against an acceptance threshold, then roll forward or roll back
• The difference between declarative and procedural configuration management, and where Ansible and Terraform fit
• Container fundamentals across Docker and Kubernetes, including control plane and worker nodes, pods, services, deployments, plus the trade-offs of Kubernetes complexity for less mature teams
• Cloud scaling and resilience strategies, including the availability benefits and management overhead of multi-cloud designs
• Application monitoring with the TIG stack, AppDynamics, and Kubernetes monitoring, using real-time and historical data

Ability

• Troubleshoot pipeline failures caused by network issues, syntax errors, and environment problems, using linting tools, Python virtual environments, and ChatOps
• Write Ansible inventories, ad hoc commands, and playbooks to configure hosts and deploy web services
• Use Terraform's init, plan, and apply workflow, along with variables, to provision cloud resources such as AWS EC2 instances
• Build network automation pipelines that validate changes in a test network before they are promoted to production
• Create Kubernetes pods, services, and deployments, and expose them with NodePorts and load balancers
• Test Python code and manage API interactions with Python as part of automated workflows