Why Learn Cisco 300-640 DCAI?

As organizations rapidly adopt Generative AI and machine learning technologies, AI infrastructure has become a critical component of enterprise data centers. Building scalable AI environments requires expertise in networking, GPU computing, storage, orchestration, monitoring, and operational best practices.

This course provides a structured learning path that aligns with Cisco's latest 300-640 DCAI exam blueprint. Learners will understand how AI workloads influence infrastructure design and how Cisco's AI-ready data center solutions support high-performance computing environments.

Whether you are designing GPU clusters, implementing AI-ready fabrics, deploying Cisco UCS environments, or managing enterprise AI infrastructure, this course equips you with the practical knowledge required to confidently implement Cisco AI infrastructure solutions.

Cisco 300-640 DCAI Course Modules

Module 1: AI Foundations and Workloads

Learn the fundamentals of AI and machine learning workloads, including Retrieval-Augmented Generation (RAG), training, inference, and Generative AI. Explore the AI lifecycle, common enterprise AI use cases, AI deployment models such as cloud, hybrid, on-premises, and edge AI, along with the key infrastructure components required for AI environments, including networking, compute, GPU deployment, virtualization, orchestration, monitoring, and storage. This module also introduces Cisco AI solutions such as AI PODs, AI Canvas, and Hyperfabric AI.

Module 2: AI Infrastructure Components and Architecture

Understand how to evaluate and design AI-ready data center infrastructure based on workload requirements. This module covers network design considerations such as bandwidth, latency, redundancy, scalability, and security; compute design factors including CPU, GPU, and memory resources; storage design for performance and capacity; and infrastructure planning for power, cooling, energy efficiency, sustainability, and hybrid AI deployments with cloud integration.

Module 3: AI Infrastructure Deployment and Data Management

Explore how to deploy high-performance AI infrastructure using Cisco Data Center technologies. Learn to configure Cisco Nexus for AI networking with features such as Priority Flow Control (PFC), Explicit Congestion Notification (ECN), Enhanced Transmission Selection (ETS), RDMA over Converged Ethernet (RoCE/RoCEv2), Quality of Service (QoS), and load distribution. This module also covers Cisco UCS configuration for AI workloads, including domain profiles, power policies, storage policies, LAN connectivity, vNIC policies, QoS policies, system classes, and NTP policies. In addition, learners will explore Cisco orchestration and automation platforms such as Nexus Dashboard, Cisco APIC, Hyperfabric, and Cisco Intersight.

Module 4: AI Infrastructure Operations and Troubleshooting

Learn how to benchmark AI infrastructure performance, implement monitoring for Cisco AI data center environments, and maintain operational reliability. This module covers monitoring using Cisco platforms such as Nexus Dashboard and Intersight, analyzing telemetry, system health, alerts, and log correlation, and troubleshooting AI infrastructure issues to ensure performance, scalability, and stability in production environments.

Recommended Experience

Basic understanding of networking concepts
Familiarity with Cisco Data Center technologies
Knowledge of virtualization and storage fundamentals
Basic understanding of AI and machine learning concepts
Experience with enterprise infrastructure is beneficial but not mandatory

Cisco 300-640 DCAI Prerequisites

There are no mandatory prerequisites for this course. However, prior knowledge of the following is recommended:

Cisco networking fundamentals
Data center architectures
Storage technologies
Virtualization concepts
Cloud computing fundamentals
Basic AI and machine learning terminology.

What You'll Learn in Cisco 300-640 DCAI

By completing this course, you will be able to:

Explain AI and machine learning workload types, including training, inference, RAG, and Generative AI.
Describe the AI lifecycle and common enterprise AI use cases.
Identify AI infrastructure deployment models, including cloud, hybrid, on-premises, and edge AI.
Understand the networking, compute, storage, virtualization, orchestration, and monitoring components of AI environments.
Evaluate AI infrastructure designs based on workload performance, scalability, redundancy, security, and sustainability.
Configure high-performance networking features such as RoCE, QoS, ECN, ETS, and congestion control for AI workloads.
Configure Cisco UCS policies to support AI infrastructure deployments.
Deploy AI-ready infrastructure using Cisco Nexus Dashboard, APIC, Hyperfabric, and Cisco Intersight.
Implement infrastructure benchmarking and performance evaluation techniques.
Monitor AI infrastructure using Cisco operational telemetry, alerts, system health, and log correlation tools.
Troubleshoot AI infrastructure to ensure high availability, scalability, and operational performance.
Prepare confidently for the Cisco 300-640 DCAI certification exam.

Who Should Take Cisco 300-640 DCAI?

Data Center Engineers and Administrators
Systems & Network Engineers
Cloud & AI Infrastructure Engineers
Infrastructure Architects
Cisco Professionals preparing for CCNP Data Center
AI Platform Engineers
IT Professionals implementing enterprise AI infrastructure

Career Opportunities After Cisco 300-640 DCAI

After completing this course, learners can pursue roles such as:

Cloud & AI Infrastructure Engineers
Cisco Data Center Engineer
Data Center Network Engineer
Systems Engineer
AI Platform Engineer
Network & Infrastructure Architects
Data Center Consultants
Enterprise Infrastructure Engineers

Skills You Will Gain

AI Infrastructure Design
Cisco Data Center Technologies
High-Performance Networking
Cisco Nexus
Cisco UCS
Cisco Intersight
Cisco Nexus Dashboard
Cisco APIC
Hyperfabric AI
GPU Infrastructure Fundamentals
RoCE and QoS Configuration
AI Infrastructure Monitoring
Infrastructure Benchmarking
Enterprise AI Deployments
AI Infrastructure Troubleshooting