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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
Cisco AI Infrastructure Architecture30%- Hyperconverged Infrastructure for AI
- Storage Architecture Considerations
- Network Architecture for AI Workloads
- Cisco UCS AI Infrastructure Solutions
AI Infrastructure Fundamentals15%- AI Ecosystem Components
- AI Infrastructure Requirements
- GPU Architecture and Types
- AI Workload Characteristics
AI Infrastructure Deployment25%- Hardware Installation and Configuration
- Network Switch Configuration
- Storage Configuration for AI
- Cabling and Connectivity
- GPU Server Configuration
AI Infrastructure Security and Compliance10%- Access Control and Authentication
- Security Best Practices
- Compliance Considerations
AI Infrastructure Operations20%- Troubleshooting Common Issues
- Performance Optimization
- Monitoring and Management Tools
- Capacity Planning

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q27-Q32):

NEW QUESTION # 27
A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?

Answer: B

Explanation:
Cisco HyperFabric AI Small AI Cluster matches the 96-GPU requirement because the validated HyperFabric reference architecture uses 12 Cisco UCS C885A M8 servers with NVIDIA HGX for a total of 96 GPUs. Cisco Nexus HyperFabric is also cloud-managed and compliant with NVIDIA Enterprise Reference Architecture, enabling centralized management of the on-premises AI network, GPU servers, and storage.


NEW QUESTION # 28
An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?

Answer: C


NEW QUESTION # 29
When considering a rail-only network design, when would adding spine switches make sense?

Answer: C

Explanation:
In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.


NEW QUESTION # 30
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?

Answer: C

Explanation:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.


NEW QUESTION # 31
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)

Answer: C,E

Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.


NEW QUESTION # 32
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