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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations | 20% | - Troubleshooting Common Issues - Capacity Planning - Monitoring and Management Tools - Performance Optimization |
| AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Compliance Considerations - Security Best Practices |
| AI Infrastructure Fundamentals | 15% | - AI Ecosystem Components - AI Infrastructure Requirements - GPU Architecture and Types - AI Workload Characteristics |
| Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Storage Architecture Considerations - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions |
| AI Infrastructure Deployment | 25% | - Storage Configuration for AI - Cabling and Connectivity - Network Switch Configuration - GPU Server Configuration - Hardware Installation and Configuration |
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NEW QUESTION # 31
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: C
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 # 32
An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements:
- Support 256 GPU servers with RDMA connectivity.
- Prevent any packet loss that causes RDMA connection failures.
- Maintain consistent low-latency communication with a target of less
than 10 microseconds.
- Use industry-standard protocols and configurations.
Which configuration ensures that RoCEv2 operates as a lossless transport?
Answer: A
Explanation:
RoCEv2 requires lossless Ethernet behavior to prevent packet drops that can disrupt RDMA communication. Enabling Priority Flow Control on the traffic class carrying RoCEv2 packets allows Ethernet pause behavior for that priority, helping maintain reliable, low-latency RDMA transport across the AI training fabric.
NEW QUESTION # 33
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: C
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 # 34
What is a function of NVLink technology within a high-performance AI server or cluster?
Answer: D
Explanation:
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.
NEW QUESTION # 35
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?
Answer: D
Explanation:
Rapid provisioning supports scalable AI workload execution by enabling computational platforms to be launched quickly and consistently as workload demand grows. This allows AI infrastructure to add or prepare compute resources efficiently for training, inference, or data-processing tasks.
NEW QUESTION # 36
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