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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Security Best Practices - Compliance Considerations |
| Topic 2: Cisco AI Infrastructure Architecture | 30% | - Cisco UCS AI Infrastructure Solutions - Storage Architecture Considerations - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI |
| Topic 3: AI Infrastructure Deployment | 25% | - GPU Server Configuration - Hardware Installation and Configuration - Cabling and Connectivity - Storage Configuration for AI - Network Switch Configuration |
| Topic 4: AI Infrastructure Fundamentals | 15% | - AI Workload Characteristics - AI Ecosystem Components - GPU Architecture and Types - AI Infrastructure Requirements |
| Topic 5: AI Infrastructure Operations | 20% | - Monitoring and Management Tools - Troubleshooting Common Issues - Capacity Planning - Performance Optimization |
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NEW QUESTION # 76
Drag and Drop Question
A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.
Answer:
Explanation:
NEW QUESTION # 77
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: A
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 # 78
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 # 79
A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment. Which Cisco Intersight capability is most critical for effectively monitoring this AI- centric infrastructure?
Answer: B
Explanation:
Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.
NEW QUESTION # 80
An engineer must optimize the performance of an AI inference workload running on a Cisco UCS C-Series rack server. The workload experiences intermittent latency spikes, and Intersight system logs show frequent thermal event entries. Which troubleshooting action must be taken first to address the performance issues on the Cisco UCS server?
Answer: D
Explanation:
Frequent thermal events indicate that the server may be throttling CPU or GPU performance to protect hardware from overheating. The first action is to verify the physical cooling conditions, including ambient temperature, airflow, and fan health, because resolving thermal constraints directly addresses the likely cause of intermittent latency spikes.
NEW QUESTION # 81
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