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| Section | Weight | Objectives |
|---|---|---|
| Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Hyperconverged Infrastructure for AI - Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads |
| AI Infrastructure Deployment | 25% | - Storage Configuration for AI - GPU Server Configuration - Hardware Installation and Configuration - Network Switch Configuration - Cabling and Connectivity |
| AI Infrastructure Security and Compliance | 10% | - Security Best Practices - Compliance Considerations - Access Control and Authentication |
| AI Infrastructure Operations | 20% | - Troubleshooting Common Issues - Performance Optimization - Monitoring and Management Tools - Capacity Planning |
| AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - AI Ecosystem Components - GPU Architecture and Types - AI Workload Characteristics |
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NEW QUESTION # 47
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?
Answer: D
Explanation:
RoCEv2 encapsulates RDMA traffic over UDP/IP, enabling routable RDMA communication across Layer 3 networks. TCP introduces retransmission and latency overhead unsuitable for high-performance GPU communication. SCTP and ICMP are not used for RoCEv2 transport.
NEW QUESTION # 48
A network architect designs connectivity for a new Cisco AI POD operating in Intersight Managed Mode. To ensure high availability and aggregate bandwidth for external network traffic, a port channel must be established using multiple 100 GbE uplink ports on the fabric interconnects to connect to the upstream core switch infrastructure. Which specific policy, applied through the domain profile, must be modified to define and configure this port channel on the fabric interconnects for the desired uplink connectivity?
Answer: A
Explanation:
In Cisco Intersight Managed Mode, fabric interconnect port roles and uplink port channels are defined in the port policy associated with the UCS domain profile. Modifying the port policy allows the engineer to configure multiple 100 GbE uplink ports as a port channel for aggregated bandwidth and high availability toward the upstream network.
NEW QUESTION # 49
Which IP-based storage transport supports flow control between directly connected devices?
Answer: C
Explanation:
NVMe/RoCE uses RDMA over Converged Ethernet and relies on lossless Ethernet behavior, including priority-based flow control between directly connected devices. This makes it suitable for high-performance AI storage traffic that needs low latency and minimal packet loss.
NEW QUESTION # 50
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
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 # 51
A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
- policy managed through cloud-based interface
- low latency between specific pairs of GPUs
- at least 2 GPUs per server
- all GPUs should be same model
- Intel CPU
- well suited for Inferencing
- customer is power conscious
Which server meets the requirements?
Answer: A
Explanation:
The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
Reference:
https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf
NEW QUESTION # 52
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