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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations | 20% | - Capacity Planning - Monitoring and Management Tools - Performance Optimization - Troubleshooting Common Issues |
| Topic 2: Cisco AI Infrastructure Architecture | 30% | - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads - Storage Architecture Considerations |
| Topic 3: AI Infrastructure Fundamentals | 15% | - AI Workload Characteristics - AI Infrastructure Requirements - AI Ecosystem Components - GPU Architecture and Types |
| Topic 4: AI Infrastructure Deployment | 25% | - Network Switch Configuration - GPU Server Configuration - Hardware Installation and Configuration - Storage Configuration for AI - Cabling and Connectivity |
| Topic 5: AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Security Best Practices - Compliance Considerations |
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NEW QUESTION # 41
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 current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?
Answer: A
Explanation:
Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.
NEW QUESTION # 42
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?
Answer: A
Explanation:
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.
NEW QUESTION # 43
Which components are part of the HyperFabric AI solution?
Answer: D
Explanation:
HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.
NEW QUESTION # 44
A network architect wants to provide high availability for AI compute nodes connected to dual Cisco Nexus switches. Which technology enables active-active uplink forwarding without STP blocking?
Answer: D
Explanation:
Virtual Port Channel (vPC) allows devices to use active-active uplinks across two Nexus switches while avoiding spanning-tree blocking. This increases bandwidth utilization and resiliency for AI clusters. GLBP and VRRP provide gateway redundancy, while PVST+ still relies on blocking redundant paths.
NEW QUESTION # 45
Which result is provided through image recognition using AI?
Answer: B
Explanation:
Image recognition uses AI to analyze digital images or video frames and identify, detect, classify, or label visual elements such as objects, people, patterns, or scenes.
NEW QUESTION # 46
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