Cisco CCNP Automation Core (350-901 AUTOCOR) v2.0

For IT leaders: Manual, line-by-line CLI changes slow your network team down and raise the risk of outages. This course helps network engineers and architects shift to infrastructure-as-code without retraining them as software developers.

Role fit: Assign it to network engineers, architects, and Team Leads who already manage Cisco environments and are preparing for the 350-901 AUTOCOR exam toward CCNP Automation. It works for a single automation champion or a network team standardizing on shared tooling.

Time investment: Plan for about 23 hours per learner across 26 skills of roughly 50–60 minutes each. That fits a weekly cadence over one quarter.

Risk and change management: The workflows reflect controls most IT Directors want in place:

• Tested before production: Changes go to a simulated test network before a CI/CD pipeline deploys them to production on merge.
• Pipeline-based deployment: GitLab CI pipelines are used to run automation scripts and target production inventory only when changes land on the main branch.
• Documentation-driven automation: Engineers work from vendor documentation, use automation tools like Ansible to apply changes, and still verify and correct results where needed.
• Responsible AI use: AI is framed as a tool that augments engineer expertise, not a replacement for it. That includes validating AI-generated changes in sandboxes and thinking through downstream effects.

How CBT Nuggets supports the outcome: Most skills end with a hands-on validation exercise in Virtual Labs. Learners practice with Ansible, NETCONF, RESTCONF, Terraform, pyATS, and GitLab pipelines against lab topologies, with no risk to your live network.

IT teams complete this training to replace error-prone, line-by-line CLI changes with tested, repeatable workflows. Throughout the course, your team works through hands-on scenarios that mirror how network changes reach production.

• Safer production changes: Your team builds a GitLab CI/CD pipeline that pushes configurations to a test topology, validates them, and then deploys to a mirrored production network only after a merge to the main branch. That gate reduces the risk of untested changes causing outages.
• Changes validated before they ship: Engineers use network simulation tools such as Cisco Modeling Labs, plus pyATS, to check interface state across devices and confirm configurations behave as expected before they touch live infrastructure.
• Consistent, code-driven configuration: Your team uses Ansible playbooks, NETCONF, RESTCONF, and Terraform to manage devices from version-controlled code in Git. They also use NetBox as a source of truth, which makes changes easier to audit and repeat.
• Responsible AI-aware automation: Engineers learn where AI systems fit within network automation architectures, including CI/CD pipelines, tool selection, and protocol choices. The course reinforces that automation is still a tool: teams should review documentation, verify results, and correct issues before changes reach production.

The result for IT Directors and Team Leads is a team that can design automation with idempotency in mind, contain the blast radius of a failure, and scale network operations without depending on manual effort.

Knowledge

• How the NetDevOps toolchain fits together: YAML and Ansible, Python automation libraries, Git, Docker and Docker Compose, GitLab CI/CD, Terraform, NetBox, and network simulation with CML or EVE-NG
• How YANG models underpin NETCONF and RESTCONF, and how REST APIs and Cisco platform APIs expose device and controller data
• The difference between imperative and declarative configuration management, and why idempotency and atomicity matter when choosing an automation approach
• How container images and instances work, and how microservices architectures limit blast radius and allow granular scaling
• Operational visibility and security foundations: streaming telemetry, logging and webhooks, encryption, PKI, and HTTP techniques
• AI fundamentals for network automation, including where AI systems fit into CI/CD architectures and why engineers still need to validate AI-assisted output against documentation, simulations, and expected network behavior

Ability

• Write Ansible playbooks that send show commands, capture output with register, and push configurations such as OSPF and VLANs
• Build NETCONF payloads by configuring a device, retrieving the result with a GET request, and reverse engineering it into a reusable template
• Manage configurations in Git and build GitLab pipelines that deploy to a test network, validate, and promote to production on merge to the main branch
• Compare device state across a testbed, and apply Terraform init, plan, and apply workflows with the IOS XE provider
• Stand up NetBox as a source of truth and test changes in CML or EVE-NG before they reach live production
• Evaluate AI-assisted recommendations in network automation workflows while applying engineering judgment to catch errors and downstream effects