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Implementing Cisco Data Center AI Infrastructure (DCAI)

Cisco
5 Days
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$4,395.00
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44 Learning Credits
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    • T87988
    • 5 days
    • 02/23/2026 - 02/27/2026
    • 9:00 AM
    • (GMT -06:00) Central Standard Time
    • Presented via WebEx
      • VC
        Virtual Classroom
        Attend any of our instructor-led classes virtually regardless of your physical location.
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    • T87989
    • 5 days
    • 04/06/2026 - 04/10/2026
    • 9:00 AM
    • (GMT -05:00) Central Daylight Time
    • Presented via WebEx
      • VC
        Virtual Classroom
        Attend any of our instructor-led classes virtually regardless of your physical location.
    Enroll Now
    • T87990
    • 5 days
    • 06/01/2026 - 06/05/2026
    • 9:00 AM
    • (GMT -05:00) Central Daylight Time
    • Presented via WebEx
      • VC
        Virtual Classroom
        Attend any of our instructor-led classes virtually regardless of your physical location.
    Enroll Now
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        Private Class
        Privately train a group of your employees at your facility, virtually, or any of our locations.
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Course Summary
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Description

The Implementing Cisco Data Center AI Infrastructure (DCAI) training is designed to equip professionals with the skills to support, secure, and optimize AI workloads within modern data center environments. This comprehensive program delves into the unique characteristics of AI/ML applications, their influence on infrastructure design, and best practices for automated provisioning. Participants will gain in-depth knowledge of security considerations for AI deployments and master day-2 operations, including monitoring and advanced troubleshooting techniques such as log correlation and telemetry analysis. Through hands-on experience, including practical application with tools like Splunk, learners will be prepared to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring optimal uptime and performance for critical organizational workloads. 

This training prepares you for the 300-640 DCAI v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification. This training also earns you 38 Continuing Education (CE) credits toward recertification. This training combines content from Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) and AI Solutions on Cisco Infrastructure Essentials (DCAIE) training. 

This training will help you:  

  • Acquire comprehensive skills to support, secure, and optimize AI workloads within modern data center environments 
  • Understand the design, implementation, and advanced troubleshooting of AI infrastructure, including network challenges and specialized hardware 
  • Gain in-depth knowledge of AI/ML concepts, generative AI, and their practical application in network management and automation 
  • Apply hands-on techniques for monitoring, diagnosing, and resolving issues, leveraging tools like Splunk and utilizing AI for enhanced productivity in network operations 
  • Prepare for the 300-640 DCAI v1.0 exam  
  • Earn 38 CE credits toward recertification 

Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 is a 90-minute exam associated with the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfies the concentration exam requirement for the CCNP Data Center certification. 

This exam tests your knowledge of AI infrastructure, including: 

  • Design 
  • Implementation 
  • Monitoring 
  • Troubleshooting

Objectives

  • Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications 
  • Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies 
  • Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection 
  • Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models 
  • Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity 
  • Describe the essential components and considerations for setting up robust AI infrastructure 
  • Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems 
  • Explore compliance standards, policies, and governance frameworks relevant to AI systems 
  • Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability 
  • Guide AI infrastructure decisions to optimize efficiency and cost 
  • Describe key network challenges from the perspective of AI/ML application requirements 
  • Describe the role of optical and copper technologies in enabling AI/ML data center workloads 
  • Describe network connectivity models and network designs 
  • Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing 
  • Migrate AI workloads to dedicated AI network 
  • Explain the mechanisms and operations of RDMA and RoCE protocols 
  • Understand the architecture and features of high-performance Ethernet fabrics 
  • Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks 
  • Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa 
  • Introduce the basic steps, challenges, and techniques regarding the data preparation process 
  • Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows 
  • Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks 
  • Understand the compute hardware required to run AI/ML solutions 
  • Understand existing intelligence and AI/ML solutions 
  • Describe virtual infrastructure options and their considerations when deploying 
  • Explain data storage strategies, storage protocols, and software-defined storage 
  • Use NDFC to configure a fabric optimized for AI/ML workloads 
  • Use locally hosted GPT models with RAG for network engineering tasks

Prerequisites

There are no prerequisites for this training. However, the knowledge and skills you are recommended to have before attending this training are: 

  • Cisco UCS compute architecture and operations 
  • Cisco Nexus switch portfolio and features 
  • Data Center core technologies 

These skills can be found in the following Cisco Learning Offerings:   

  • Introducing Cisco Unified Computing System (DCIUCS) 
  • Implementing Cisco NX-OS Switches and Fabrics in the Data Center (DCNX) 
  • Cisco Data Center Nexus Dashboard Essentials (DCNDE) 
  • Implementing Cisco Data Center Core Technologies (DCCOR) 

Who Should Attend

  • Network Designers 
  • Network Administrators 
  • Storage Administrators 
  • Network Engineers 
  • Systems Engineers 
  • Data Center Engineers 
  • Consulting Systems Engineers 
  • Technical Solutions Architects 
  • Cisco Integrators/Partners 
  • Field Engineers 
  • Server Administrators 
  • Network Managers 
  • Program Managers 
  • Project Managers 

Outline

Course Outline

  • Fundamentals of AI 
  • Generative AI 
  • AI Use Cases 
  • AI-ML Clusters and Models 
  • AI Toolset—Jupyter Notebook 
  • AI Infrastructure 
  • AI Workloads Placement and Interoperability 
  • AI Policies 
  • AI Sustainability 
  • AI Infrastructure Design 
  • Key Network Challenges and Requirements for AI Workloads 
  • AI Transport 
  • Connectivity Models 
  • AI Network 
  • Architecture Migration to AI/ML Network 
  • Application-Level Protocols 
  • High-Throughput Converged Fabrics 
  • Building Lossless Fabrics 
  • Congestion Visibility 
  • Data Preparation for AI 
  • AI/ML Workload Data Performance 
  • AI-Enabling Hardware 
  • Compute Resources 
  • Compute Resource Solutions 
  • Virtual Resources 
  • Storage Resources 
  • Setting Up AI Cluster 
  • Deploy and Use Open Source GPT Models for RAG 
  • AI Infrastructure Operations and Monitoring 
  • Troubleshooting AI Infrastructure 
  • Troubleshoot Common Issues in AI/ML Fabric 

Lab Outline

  • AI Toolset—Jupyter Notebook 
  • AI/ML Workload Data Performance 
  • Setting Up AI Cluster 
  • Deploy and Use Open Source GPT Models for RAG 
  • Troubleshoot Common Issues in AI/ML Fabric