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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - GPU Architecture and Types - AI Workload Characteristics - AI Ecosystem Components |
| AI Infrastructure Deployment | 25% | - Cabling and Connectivity - GPU Server Configuration - Hardware Installation and Configuration - Storage Configuration for AI - Network Switch Configuration |
| AI Infrastructure Operations | 20% | - Capacity Planning - Monitoring and Management Tools - Troubleshooting Common Issues - Performance Optimization |
| Cisco AI Infrastructure Architecture | 30% | - Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads - Storage Architecture Considerations - Hyperconverged Infrastructure for AI |
| AI Infrastructure Security and Compliance | 10% | - Security Best Practices - Access Control and Authentication - Compliance Considerations |
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NEW QUESTION # 49
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?
Answer: C
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 # 50
A medical company has existing third-party AI servers and NVMe storage, but they want to purchase Cisco network switches with a cloud managed feature for simplicity. Which solution best meets the requirements?
Answer: C
Explanation:
Bring Your Own AI HyperFabric is the best fit when the customer already has third-party AI servers and NVMe storage but wants Cisco 6000 Series switching with simplified cloud-managed fabric operations. The Premier license aligns with the AI-focused HyperFabric use case, while Cisco describes Nexus HyperFabric as a cloud-managed AI infrastructure solution using Cisco
6000 Series switches for AI workloads.
NEW QUESTION # 51
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)
Answer: A,B
Explanation:
Cisco Intersight provides GPU-focused telemetry for UCS servers, including utilization and performance metrics. These measurements allow the administrator to monitor how heavily the GPUs are being used and whether the AI workload is receiving the expected GPU performance.
NEW QUESTION # 52
A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?
Answer: A
Explanation:
ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.
NEW QUESTION # 53
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
Explanation:
Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.
NEW QUESTION # 54
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