DCAI

Please note: The exam will only be available from February 09, 2026.

The "Implementing Cisco Data Center AI Infrastructure (DCAI)" training provides professionals with the knowledge required to support, secure and optimize AI workloads in modern data center environments. This comprehensive training takes an in-depth look at the unique characteristics of AI/ML applications, their impact on infrastructure planning and best practices for automated deployment. Participants will gain in-depth knowledge of security aspects of 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 the practical application of tools such as Splunk, learners will be prepared to efficiently monitor, diagnose and resolve issues in AI/ML-enabled data centers to ensure optimal availability and performance for critical enterprise workloads.

This training prepares you for the 300-640 DCAI exam. Upon successful completion, you will receive the "Cisco Certified Specialist - Data Center AI Infrastructure" certification and meet the requirements of the specialization exam for the "Cisco Certified Network Professional (CCNP) Data Center" certification.

• 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

You will receive the original course documentation from Cisco in English language as a Cisco E-Book.

• 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

There are no prerequisites for this training course. However, it is recommended that you have the following knowledge and skills before attending this training course:

• Cisco UCS computer architecture and operation
• Cisco Nexus switch portfolio and features
• Core technologies for data centers

You can acquire these skills in the following Cisco training offerings:

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

• Acquire comprehensive skills to support, secure and optimize AI workloads in modern data center environments.
• Understand the design, implementation and advanced troubleshooting of AI infrastructures, including network challenges and specialized hardware.
• Acquire in-depth knowledge of AI/ML concepts, generative AI and their practical application in network management and automation.
• Apply practical techniques to monitor, diagnose and troubleshoot issues, utilize tools such as Splunk, and use AI to increase productivity in network operations
• Prepare for the 300-640 DCAI v1.0 exam.

This training combines content from the Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) and AI Solutions on Cisco Infrastructure Essentials (DCAIE) courses.