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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 15% | - AI Ecosystem Components - GPU Architecture and Types - AI Infrastructure Requirements - AI Workload Characteristics |
| Topic 2: Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads |
| Topic 3: AI Infrastructure Operations | 20% | - Troubleshooting Common Issues - Capacity Planning - Monitoring and Management Tools - Performance Optimization |
| Topic 4: AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Compliance Considerations - Security Best Practices |
| Topic 5: AI Infrastructure Deployment | 25% | - GPU Server Configuration - Hardware Installation and Configuration - Storage Configuration for AI - Network Switch Configuration - Cabling and Connectivity |
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NEW QUESTION # 15
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?
Answer: B
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 # 16
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: B
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 # 17
An Intersight administrator plans to deploy new server solutions to several small branch offices across the country. Each location needs at least one chassis. The servers must have redundant CPUs, memory, and 200 Gbps of unified fabric connectivity per compute node. Which hybrid AI compute solution meets the requirements?
Answer: B
Explanation:
Cisco UCS X-Direct is designed for smaller distributed or branch deployments that still need chassis-based UCS X-Series compute capabilities. It supports redundant compute resources and high-speed unified fabric connectivity, including 200 Gbps per compute node, making it the best fit for hybrid AI workloads across multiple branch office locations.
NEW QUESTION # 18
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?
Answer: B
Explanation:
Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.
NEW QUESTION # 19
Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?
Answer: A
Explanation:
A UCS Domain profile is deployed to fabric interconnects in Cisco Intersight Managed Mode and contains the domain-level configuration, including port policies, VLANs, VSANs, network connectivity, and other fabric interconnect settings.
NEW QUESTION # 20
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