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Cisco 300-640 Exam Syllabus Topics:

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

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q77-Q82):

NEW QUESTION # 77
A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

Answer: D

Explanation:
RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.


NEW QUESTION # 78
What describes inference traffic patterns in AI deployments?

Answer: C

Explanation:
Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.


NEW QUESTION # 79
A data center team enables jumbo frames in a Cisco AI cluster. What is the primary benefit of increasing the MTU for east-west GPU traffic?

Answer: C

Explanation:
Jumbo frames reduce the number of packets needed to transmit large AI datasets, decreasing CPU interrupts and protocol overhead. This improves throughput and efficiency for GPU communication. Larger MTUs do not directly affect convergence or eliminate buffering, and they typically reduce rather than increase processing overhead.


NEW QUESTION # 80
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?

Answer: A

Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.


NEW QUESTION # 81
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?

Answer: A


NEW QUESTION # 82
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