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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations | 20% | - Performance Optimization - Troubleshooting Common Issues - Capacity Planning - Monitoring and Management Tools |
| Topic 2: AI Infrastructure Deployment | 25% | - Hardware Installation and Configuration - Network Switch Configuration - Storage Configuration for AI - GPU Server Configuration - Cabling and Connectivity |
| Topic 3: Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions - Storage Architecture Considerations |
| Topic 4: AI Infrastructure Fundamentals | 15% | - AI Ecosystem Components - AI Workload Characteristics - GPU Architecture and Types - AI Infrastructure Requirements |
| Topic 5: AI Infrastructure Security and Compliance | 10% | - Compliance Considerations - Security Best Practices - Access Control and Authentication |
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NEW QUESTION # 28
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?
Answer: C
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 # 29
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?
Answer: A
NEW QUESTION # 30
What does workload distribution offer in an AI infrastructure with local and external resources?
Answer: D
Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.
NEW QUESTION # 31
A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?
Answer: B
Explanation:
Priority Flow Control (PFC) allows selective pausing of traffic classes during congestion, which is critical for RoCEv2 environments carrying GPU-to-GPU communication. AI workloads are highly sensitive to packet loss and retransmissions. BPDU Guard and DHCP Snooping are security features, while Storm Control only limits broadcast or multicast traffic and does not create lossless behavior for RDMA traffic.
NEW QUESTION # 32
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: B
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 # 33
......
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