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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q73-Q78):

NEW QUESTION # 73
A network team is configuring quality of service (QoS) for RoCEv2 traffic on a VXLAN fabric. The team must ensure that congestion notification packets (CNP) generated by storage targets are never dropped in the network, as these packets are critical for congestion control feedback. How must CNP packets be handled in the QoS configuration to ensure reliable delivery?

Answer: D

Explanation:
CNP packets provide congestion feedback for RoCEv2 traffic and must be delivered reliably so senders can reduce their transmission rate when congestion is detected. Classifying CNP packets into a high-priority queue based on their DSCP marking ensures that these control packets receive preferential treatment and are not dropped during congestion.


NEW QUESTION # 74
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?

Answer: C

Explanation:
Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.


NEW QUESTION # 75
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?

Answer: D

Explanation:
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.


NEW QUESTION # 76
Drag and Drop Question

Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.

Answer:

Explanation:


NEW QUESTION # 77
Which components are part of the HyperFabric AI solution?

Answer: D

Explanation:
HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.


NEW QUESTION # 78
......

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