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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 15% | - GPU Architecture and Types - AI Workload Characteristics - AI Infrastructure Requirements - AI Ecosystem Components |
| Topic 2: AI Infrastructure Security and Compliance | 10% | - Compliance Considerations - Access Control and Authentication - Security Best Practices |
| Topic 3: Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI - Storage Architecture Considerations |
| Topic 4: AI Infrastructure Operations | 20% | - Performance Optimization - Troubleshooting Common Issues - Capacity Planning - Monitoring and Management Tools |
| Topic 5: AI Infrastructure Deployment | 25% | - Cabling and Connectivity - Hardware Installation and Configuration - Storage Configuration for AI - Network Switch Configuration - GPU Server Configuration |
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NEW QUESTION # 42
When considering a rail-only network design, when would adding spine switches make sense?
Answer: D
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 # 43
Refer to the exhibit. Users report delayed response times and network latency when running certain AI workloads on a specific Cisco UCS Intersight Managed Mode Domain. Based on the fault message and reported symptoms, which troubleshooting action must be taken first to address the network latency issues?
Answer: A
Explanation:
The fault shows a receive error statistics threshold crossing on switch B port 11. Because receive errors on a physical interface commonly indicate cabling, transceiver, or physical-layer issues, the first troubleshooting step is to inspect the physical connection and SFP+ components on the affected port before investigating workload or higher-layer configuration.
NEW QUESTION # 44
What describes inference traffic patterns in AI deployments?
Answer: A
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 # 45
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?
Answer: A
Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.
NEW QUESTION # 46
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?
Answer: D
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 # 47
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