Cisco ACI is a policy-driven CLOS, or Spine-Leaf, switching fabric that utilizes Layer 3 ECMP routing in the underlay and VXLAN encapsulation in the overlay to transport Layer 2 and Layer 3 traffic both east-west within the fabric and north-south between the fabric and external networks. ACI consists of the Application Policy Infrastructure Controller (APIC), which provides centralized policy, automation, and fabric management capabilities. Leaf switches function as Top-of-Rack (ToR) switches that provide connectivity to servers, storage, and external networks, while spine switches provide high-speed Layer 3 ECMP connectivity between all leaf switches. An ACI fabric can be scaled horizontally by adding additional leaf switches, connecting them to the spine layer, and registering them with the fabric. Cisco ACI was architected to operate as a distributed "single logical system," similar to a modular chassis-based architecture, where APICs provide centralized policy and control functions, spine switches provide the high-speed fabric core, and leaf switches provide endpoint connectivity. This distributed architecture enables organizations to decouple these functions from the constraints of a physical chassis and deploy them anywhere within the data center environment. Cisco ACI further extends this architecture through remote leaf, multi-pod, and multi-site deployments. Remote leaf allows organizations to extend policy and connectivity to remote locations while maintaining centralized operational control. Multi-pod enables geographically separated pods to operate as a single fabric, while multi-site supports independent ACI fabrics interconnected across multiple data centers or cloud environments. With Cisco Nexus Dashboard and Nexus Dashboard Orchestrator (NDO) 4.x, organizations can centrally manage policy, orchestration, analytics, and day-two operations across multiple ACI fabrics and cloud-integrated environments from a unified operational platform. Nexus Dashboard 4.x enhances scalability, resiliency, automation, and operational visibility while integrating advanced analytics, AI-driven assurance, telemetry, and cloud-native application support. This enables consistent policy enforcement, simplified operations, and unified visibility across on-premises data centers, edge environments, and public cloud deployments.

This learning path on Intermediate Enhanced Interior Gateway Routing Protocol (EIGRP) provides a comprehensive understanding of EIGRP routing in Cisco networks. Participants will explore critical topics such as route summarization, route filtering, and redistribution. This path also delves into specialized EIGRP features, including offset lists and variance for unequal-cost load-balancing. Throughout, the learning path emphasizes best practices, common design mistakes to avoid, and hands-on configuration strategies to help learners confidently apply EIGRP in complex, real-world environments.

This learning path builds a practical understanding of IPv6 in enterprise environments through a structured, hands-on approach. It begins with foundational routing using OSPFv3 in a multi-area design, then expands into multi-protocol environments with EIGRP for IPv6 and eBGP between autonomous systems. As you progress, you will apply key routing control techniques such as filtering, summarization, and redistribution to manage and optimize IPv6 route propagation across domains. The learning path concludes with IPv6 transition technologies, including NAT64 and IPv6 tunneling, showing how IPv6 networks integrate with existing IPv4 infrastructure during real-world migration scenarios.

This learning path on foundational Enhanced Interior Gateway Routing Protocol (EIGRP) provides a comprehensive introduction and deep dive into the core aspects of EIGRP. It starts by explaining the basics of EIGRP, including its purpose, operation, and fundamental concepts such as EIGRP Neighbors, advertising routes, authentication, and many others. This learning path aims to equip network professionals with the knowledge needed to manage and optimize EIGRP in real-world environments, emphasizing best practices and common pitfalls.