Cisco 300-640 DCAI: Data Center AI Infrastructure
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This course contains the use of artificial intelligence.
Artificial intelligence has moved from the lab into the data center, and the network underneath it is no longer ordinary. Training and inference workloads demand lossless fabrics, GPU-dense compute, high-throughput storage, and telemetry that actually tells you what is happening. This course teaches you how to design, build, and operate that infrastructure on Cisco — and prepares you to pass the 300-640 DCAI (Implementing Cisco Data Center AI Infrastructure) exam, a concentration toward your CCNP Data Center certification.
We start with the fundamentals: what AI, machine learning, deep learning, and generative AI actually are, the workloads they create, and where they run — cloud, hybrid, on-prem, and edge. From there we move into network design for AI: bandwidth, latency, scalability, and the non-blocking, lossless fabric requirements that make or break a cluster. You will go deep on the technologies that matter for the exam and the job — RDMA and RoCEv2, PFC, ECN, and ETS congestion management, intelligent buffers, and QoS on the Nexus 9000 family.
Then we cover compute and storage: CPUs, GPUs, DPUs, SmartNICs and NVIDIA BlueField, Cisco UCS C- and X-Series, UCS management through Intersight, and modern storage with NVMe and NVMe-oF. Finally, you will deploy and operate a fabric with Nexus Dashboard Fabric Controller, stand up open-source GPT for RAG, and troubleshoot with telemetry and Splunk.
And you will not just watch — you will practice. The course includes seven downloadable, step-by-step hands-on lab guides that walk you through the real workflows: standing up an AI cluster fabric with NDFC, measuring RoCEv2 workload performance, deploying open-source GPT for RAG, troubleshooting an AI/ML fabric with Splunk, and more — each ready to follow in your own lab or a free Cisco dCloud environment. You also get two full practice tests with 130 exam-style questions, every answer backed by a detailed explanation, so you can find your weak spots and walk into test day knowing exactly what to expect.
Every lesson is tight, visual, and exam-aligned. By the end you will be able to read an AI infrastructure requirement and design the Cisco solution that meets it — with confidence on test day and on the job.
Working knowledge of data center networking (switching, routing, VLANs/VXLAN) - roughly CCNA to CCNP Data Center level
Familiarity with basic data center compute and storage concepts is helpful but not required
No AI or machine-learning background needed - we build it from the ground up
No lab gear required to follow along; access to Cisco Nexus/UCS or Nexus Dashboard helps for hands-on practice
Explain AI, ML, deep learning, and generative AI workloads and map each to its infrastructure demands
Design AI-ready data center networks: bandwidth, latency, scalability, redundancy, and non-blocking lossless fabrics
Build lossless Ethernet fabrics with RDMA/RoCEv2, PFC, ECN, ETS, intelligent buffers, and QoS on Cisco Nexus 9000
Select and size AI compute and storage: GPUs, DPUs, SmartNICs/BlueField, Cisco UCS, and NVMe/NVMe-oF
Manage UCS compute with Cisco Intersight domain, server profiles, and policies
Deploy and operate AI fabrics with Nexus Dashboard Fabric Controller, RAG with open-source GPT, and Splunk telemetry
Prepare confidently for every domain of the Cisco 300-640 DCAI exam
Practice the real workflows with 7 downloadable, step-by-step hands-on lab guides — NDFC, RoCEv2 performance, RAG, Splunk, and more
Test your readiness with 2 full practice exams (130 questions), each answer with a detailed explanation
Network and data center engineers preparing for the Cisco 300-640 DCAI exam / CCNP Data Center concentration
Infrastructure professionals tasked with designing or supporting AI/ML clusters
Architects who need to translate AI workload requirements into Cisco data center designs
Anyone who wants a clear, current, exam-aligned path into AI data center infrastructure
