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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
AI Infrastructure Fundamentals20%- Explain Cisco AI infrastructure solutions and portfolio
- Describe types of AI infrastructure: on-premises, cloud, hybrid, edge
- Describe AI and machine learning concepts
- Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring
- Describe AI use cases in data center environments
AI Infrastructure Components and Architecture30%- Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures
- Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS
- Evaluate hybrid and multi-cloud AI integration
- Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability
- Evaluate power, cooling, and efficiency requirements
AI Infrastructure Deployment and Data Management30%- Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics
- Deploy and manage AI infrastructure with orchestration tools
- Implement data management and lifecycle for AI workloads
- Configure compute and storage using Cisco UCS: profiles, policies, Intersight
- Integrate AI frameworks and applications with Cisco infrastructure
AI Infrastructure Operations and Troubleshooting20%- Manage health, logs, and events
- Troubleshoot network, compute, storage, and orchestration issues
- Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts
- Optimize performance, security, and reliability
- Implement performance benchmarking and validation

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q72-Q77):

NEW QUESTION # 72
An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?

Answer: A

Explanation:
RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.


NEW QUESTION # 73
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 # 74
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

Answer: B

Explanation:
Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.


NEW QUESTION # 75
A corporate network administrator manages a HyperFabric AI environment and wants to receive email notifications about assertions in the fabric. The administrator sets up a recipient email endpoint under the notification endpoints in the controller, but notifications are not received.
Which two configurations cause the issue? (Choose two.)

Answer: D,E

Explanation:
Email endpoints must be verified before they can receive HyperFabric notifications, and the endpoint must also be included with the relevant fabric in the notification policy. Creating the email endpoint alone does not activate assertion notifications for a fabric until both of these notification requirements are completed.


NEW QUESTION # 76
What does workload distribution offer in an AI infrastructure with local and external resources?

Answer: B

Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.


NEW QUESTION # 77
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