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| Section | Objectives |
|---|---|
| Topic 1: Security and Governance in AI Data Centers | - Infrastructure security
|
| Topic 2: Data Center AI Infrastructure Fundamentals | - AI/ML workload requirements in data centers
|
| Topic 3: Data Center Networking for AI Workloads | - Cisco Data Center networking technologies
|
| Topic 4: AI Infrastructure Deployment and Operations | - Deployment models
|
| Topic 5: Compute and Storage for AI Infrastructure | - Storage systems
|
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問題 #59
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?
答案：D
解題說明：
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.
問題 #60
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?
答案：D
解題說明：
Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.
問題 #61
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?
答案：C
解題說明：
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
問題 #62
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?
答案：C
解題說明：
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.
問題 #63
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?
答案：C
解題說明：
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.
問題 #64
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