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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment | 25% | - Storage Configuration for AI - Hardware Installation and Configuration - GPU Server Configuration - Cabling and Connectivity - Network Switch Configuration |
| AI Infrastructure Operations | 20% | - Capacity Planning - Monitoring and Management Tools - Performance Optimization - Troubleshooting Common Issues |
| AI Infrastructure Security and Compliance | 10% | - Security Best Practices - Compliance Considerations - Access Control and Authentication |
| Cisco AI Infrastructure Architecture | 30% | - Storage Architecture Considerations - Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads - Hyperconverged Infrastructure for AI |
| AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - AI Workload Characteristics - AI Ecosystem Components - GPU Architecture and Types |
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NEW QUESTION # 54
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: C
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 # 55
Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?
Answer: A
Explanation:
A UCS Domain profile is deployed to fabric interconnects in Cisco Intersight Managed Mode and contains the domain-level configuration, including port policies, VLANs, VSANs, network connectivity, and other fabric interconnect settings.
NEW QUESTION # 56
An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?
Answer: A
Explanation:
VAST Data Universal Storage provides a software-defined, disaggregated shared-everything storage architecture designed for AI-scale workloads. It supports Cisco UCS-based deployments managed through Intersight and delivers high-performance all-flash storage with RDMA and GPU Direct Storage capabilities for large-scale AI training, fine-tuning, and inference environments.
NEW QUESTION # 57
A Cisco AI environment requires synchronized packet processing timestamps across network devices for analytics accuracy. Which protocol should be deployed?
Answer: D
Explanation:
Precision Time Protocol (PTP) provides highly accurate clock synchronization required for latency-sensitive AI and analytics environments. NTP offers lower precision suitable for general IT systems. FTP and DNS are unrelated to time synchronization.
NEW QUESTION # 58
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 # 59
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