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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Deployment | 25% | - Cabling and Connectivity - Hardware Installation and Configuration - Network Switch Configuration - Storage Configuration for AI - GPU Server Configuration |
| Topic 2: AI Infrastructure Fundamentals | 15% | - AI Ecosystem Components - AI Infrastructure Requirements - GPU Architecture and Types - AI Workload Characteristics |
| Topic 3: AI Infrastructure Security and Compliance | 10% | - Security Best Practices - Access Control and Authentication - Compliance Considerations |
| Topic 4: Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions - Storage Architecture Considerations |
| Topic 5: AI Infrastructure Operations | 20% | - Monitoring and Management Tools - Capacity Planning - Troubleshooting Common Issues - Performance Optimization |
>> 300-640 Test Questions Answers <<
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NEW QUESTION # 37
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 # 38
A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?
Answer: D
Explanation:
Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.
NEW QUESTION # 39
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: A
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 # 40
An end customer plans to deploy an AI fabric in a new data center. Their engineers are more familiar with CLI-based networks and require the capability to automate standardized configuration templates and monitor flows for the fabric. Which data center automation tool meets the requirements?
Answer: C
Explanation:
Nexus Dashboard is the Cisco data center automation and operations platform used with Nexus fabrics. It supports standardized fabric configuration through automation templates and provides visibility features such as flow monitoring, making it the appropriate tool for engineers who need CLI-oriented data center fabric operations with automation and telemetry.
NEW QUESTION # 41
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?
Answer: C
Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.
NEW QUESTION # 42
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