Implementing Cisco Data Center AI Infrastructure 300-640 DCAI

Course Overview

The Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) training and certification program is designed for data center engineers, AI infrastructure specialists, network engineers, cloud architects, and IT professionals who want to develop advanced expertise in building and managing AI-ready data center environments.

This certification validates your ability to deploy, configure, optimize, and manage infrastructure designed for Artificial Intelligence (AI), Machine Learning (ML), and high-performance computing (HPC) workloads. The course focuses on modern AI infrastructure architecture, high-speed networking, GPU-enabled compute, storage optimization, automation, and workload security.

Offered by Linux Training Center, Coimbatore, this course aligns with the official Cisco 300-640 DCAI exam objectives and provides hands-on practical training in AI data center design, networking, compute infrastructure, storage systems, automation, and security.

Who Should Enroll?

  • Data center engineers managing enterprise infrastructure
  • AI infrastructure engineers
  • Network engineers working with high-performance environments
  • Cloud architects designing AI-ready systems
  • Infrastructure specialists supporting AI workloads
  • DevOps engineers handling AI platform deployment
  • IT professionals pursuing Cisco Data Center certifications

Why This Course Stands Out

  • Complete coverage of Cisco 300-640 DCAI exam objectives
  • Hands-on labs with AI-ready infrastructure scenarios
  • Practical training for GPU, HPC, and AI workload environments
  • Strong focus on high-performance networking and compute optimization
  • Real-world enterprise AI infrastructure use cases
  • Certification-focused mock exams and assessments
  • Industry-aligned curriculum for modern AI infrastructure roles

Why Choose Linux Training Center, Coimbatore?

  • Expert instructors with Cisco and AI infrastructure expertise
  • Advanced practical labs with enterprise AI infrastructure environments
  • Flexible weekday and weekend batch schedules
  • Comprehensive study materials and lab access
  • Mock exams and certification-focused preparation
  • Career guidance and placement assistance
  • Post-training mentorship until certification completion

Become an AI Infrastructure Expert

Advance your data center career with Cisco 300-640 DCAI certification training. Gain expertise in AI-ready networking, GPU compute, storage optimization, automation, and secure infrastructure operations to excel in modern AI-driven enterprise environments.

Implementing Cisco Data Center AI Infrastructure 300-640 DCAI

Modules

AI Fundamentals and Applications
  • Describe AI/ML workload types
  • RAG
  • Training
  • Inference
  • Generative AI
  • Describe the AI lifecycle
  • Describe AI use cases
  • Describe the types of AI infrastructure
  • Cloud
  • Hybrid
  • On-premises
  • Edge AI
  • Describe the components used for AI environments
  • Network
  • Compute and GPUs deployment (NVLink)
  • Virtualization and containerization
  • Orchestration
  • Monitoring
  • Storage such as SAN, Fibre Channel, NVMe, Block and File
  • Describe Cisco AI solutions
  • AI PODs
  • AI Canvas
  • Hyperfabric AI
    • AI Infrastructure Components and Architecture
  • Evaluate network deployment based on AI workload requirements such as bandwidth, latency, redundancy, scalability, and security
  • Evaluate compute deployment based on AI workload requirements such as CPU resources, GPU resources and connectivity, memory, virtualization support, scalability redundancy, and workload types
  • Evaluate storage deployment based on AI workload requirements such as capacity, performance, redundancy and availability, and scalability
  • Evaluate power, efficiency, and sustainability based on AI workload requirements such as power and cooling, power usage effectiveness, and renewable energy
  • Evaluate hybrid AI deployment with cloud integration such as secure connectivity, data synchronization, and workload mobility
    • AI Infrastructure Deployment and Data Management
  • Configure high-performance networks to support AI workloads using Cisco Data Center
  • Congestion control mechanisms (PFC, ECN, ETS)
  • RDMA over Converged Ethernet (RoCE, RoCEv2)
  • Quality of service (QoS)
  • Load distribution
  • Configure high-performance compute and storage to support AI workloads using Cisco UCS
  • Domain profiles
  • Power policy
  • Storage policies
  • LAN connectivity and vNIC policies
  • QoS policies and system classes
  • NTP policy
  • Deploy AI-ready fabrics using Cisco orchestration tools
  • Nexus Dashboard
  • APIC
  • Hyperfabric
  • Intersight
    • AI Infrastructure Operations and Troubleshooting
  • Implement benchmarks to evaluate AI infrastructure performance
  • Implement monitoring of AI data center infrastructures using Cisco solutions such as Nexus Dashboard and Intersight
  • Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance
  • Operational telemetry
  • System health
  • Alerts
  • Log correlation
  • Troubleshoot AI infrastructure using system messages and management tools

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