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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Compliance Considerations - Security Best Practices |
| Topic 2: AI Infrastructure Operations | 20% | - Troubleshooting Common Issues - Capacity Planning - Performance Optimization - Monitoring and Management Tools |
| Topic 3: AI Infrastructure Deployment | 25% | - Cabling and Connectivity - Storage Configuration for AI - Network Switch Configuration - Hardware Installation and Configuration - GPU Server Configuration |
| Topic 4: Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI - Storage Architecture Considerations |
| Topic 5: AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - AI Ecosystem Components - AI Workload Characteristics - GPU Architecture and Types |
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NEW QUESTION # 36
What is the process involved in workload scheduling for AI environments?
Answer: B
Explanation:
Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.
NEW QUESTION # 37
A corporate network administrator manages a HyperFabric AI environment and wants to receive email notifications about assertions in the fabric. The administrator sets up a recipient email endpoint under the notification endpoints in the controller, but notifications are not received.
Which two configurations cause the issue? (Choose two.)
Answer: C,E
Explanation:
Email endpoints must be verified before they can receive HyperFabric notifications, and the endpoint must also be included with the relevant fabric in the notification policy. Creating the email endpoint alone does not activate assertion notifications for a fabric until both of these notification requirements are completed.
NEW QUESTION # 38
A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?
Answer: D
Explanation:
Cisco HyperFabric AI Small AI Cluster matches the 96-GPU requirement because the validated HyperFabric reference architecture uses 12 Cisco UCS C885A M8 servers with NVIDIA HGX for a total of 96 GPUs. Cisco Nexus HyperFabric is also cloud-managed and compliant with NVIDIA Enterprise Reference Architecture, enabling centralized management of the on-premises AI network, GPU servers, and storage.
NEW QUESTION # 39
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: D
NEW QUESTION # 40
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 # 41
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