The Implementing Cisco Data Center AI Infrastructure v1.0 (DCAI 300-640) is a 90-minute exam that is part of the CCNP Data Center certification track. This exam assesses your expertise in designing, implementing, monitoring, and troubleshooting AI infrastructure, covering areas such as networking, computing, storage, and orchestration. Successful completion of this exam awards the Cisco Certified Specialist - Data Center AI Infrastructure certification and contributes to achieving the Cisco Certified Network Professional (CCNP) Data Center certification. SPOTO provides the most up-to-date 300-640 exam questions to help candidates prepare effectively and pass the Cisco Data Center exam quickly. With SPOTO's Cisco 300-640 DCAI practice tests, you can increase your chances of passing the exam on your first attempt.
The Implementing Cisco Data Center AI Infrastructure v1.0 (DCAI 300-640) is a 90-minute exam that is part of the CCNP Data Center certification track. This exam assesses your expertise in designing, implementing, monitoring, and troubleshooting AI infrastructure, covering areas such as networking, computing, storage, and orchestration. Successful completion of this exam awards the Cisco Certified Specialist - Data Center AI Infrastructure certification and contributes to achieving the Cisco Certified Network Professional (CCNP) Data Center certification. SPOTO provides the most up-to-date 300-640 exam questions to help candidates prepare effectively and pass the Cisco Data Center exam quickly. With SPOTO's Cisco 300-640 DCAI practice tests, you can increase your chances of passing the exam on your first attempt.
The following topics are general guidelines for the content likely to be included on the exam.
1.0 AI Fundamentals and Applications(20%)
1.1 Describe AI/ML workload types
1.2 Describe the AI lifecycle
1.3 Describe AI use cases
1.4 Describe the types of AI infrastructure
1.5 Describe the components used for AI environments
1.6 Describe Cisco AI solutions
2.0 AI Infrastructure Components and Architecture (30%)
2.1 Evaluate network deployment based on AI workload requirements such as bandwidth, latency, redundancy, scalability, and security
2.2 Evaluate compute deployment based on AI workload requirements such as CPU resources, GPU resources and connectivity, memory, virtualization support, scalability, redundancy, and workload types
2.3 Evaluate storage deployment based on AI workload requirements such as capacity, performance, redundancy and availability, and scalability
2.4 Evaluate power, efficiency, and sustainability based on AI workload requirements such as power and cooling, power usage effectiveness, and renewable energy
2.5 Evaluate hybrid AI deployment with cloud integration such as secure connectivity, data synchronization, and workload mobility
3.0 AI Infrastructure Deployment and Data Management (30%)
3.1 Configure high-performance networks to support AI workloads using Cisco Data Center
3.2 Configure high-performance compute and storage to support AI workloads using Cisco UCS
3.3 Deploy AI-ready fabrics using Cisco orchestration tools
4.0 AI Infrastructure Operations and Troubleshooting (20%)
4.1 Implement benchmarks to evaluate AI infrastructure performance
4.2 Implement monitoring of AI data center infrastructures using Cisco solutions such as Nexus Dashboard and Intersight
4.3 Monitor AI infrastructure using system messages and management tools to ensure reliability, scalability and performance
4.4 Troubleshoot AI infrastructure using system messages and management tools
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