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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment | 25% | - Cabling and Connectivity - Network Switch Configuration - Storage Configuration for AI - GPU Server Configuration - Hardware Installation and Configuration |
| Cisco AI Infrastructure Architecture | 30% | - Network Architecture for AI Workloads - Cisco UCS AI Infrastructure Solutions - Storage Architecture Considerations - Hyperconverged Infrastructure for AI |
| AI Infrastructure Fundamentals | 15% | - GPU Architecture and Types - AI Infrastructure Requirements - AI Workload Characteristics - AI Ecosystem Components |
| AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Security Best Practices - Compliance Considerations |
| AI Infrastructure Operations | 20% | - Monitoring and Management Tools - Performance Optimization - Troubleshooting Common Issues - Capacity Planning |
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NEW QUESTION # 71
Which result is provided through image recognition using AI?
Answer: A
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 # 72
A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?
Answer: B
Explanation:
RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.
NEW QUESTION # 73
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: A
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 # 74
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 # 75
An engineer deploys an AI fabric on Cisco Nexus 9000 Series Switches connected to Cisco UCS nodes with NVIDIA GPUs. The requirements call for optimal network performance for training workloads. Adaptive routing is enabled on the NICs, and per-packet load balancing is enabled on the switches. Which other configuration must be implemented for this integration to be fully operational?
Answer: A
Explanation:
The NIC must be explicitly configured with the Cisco dynamic load-balancing setting so it recognizes that the connected Cisco Nexus fabric supports adaptive routing. This completes the integration between NIC-side adaptive routing and switch-side per-packet load balancing for optimized AI training traffic.
NEW QUESTION # 76
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