Cisco 300-640 - Implementing Cisco Data Center AI Infrastructure (DCAI)

The UCS X9516 X-Fabric Module provides PCIe Gen5 connectivity between supported M8 compute nodes and X580p PCIe nodes. Before enabling that connectivity, the affected compute nodes must be powered down and equipped with the appropriate UCS PCI Gen5 mezzanine cards .

Cisco identifies the UCSX-V5-PCIME rear mezzanine card as a required hardware component for X580p connectivity. The card provides PCIe Gen5 electrical lanes between each CPU and the X9516 X-Fabric modules. Cisco also specifies that companion compute-node host power must be off for insertion or removal operations involving X580p PCIe connectivity, and that X9516 modules must be installed before the X580p PCIe nodes are installed.

Therefore, powering down the affected blades and installing their required PCI mezzanine cards are the appropriate preparatory actions.

The IFMs provide normal UCS network connectivity and do not need to be powered off or disconnected from Fabric Interconnects merely to install the X-Fabric modules. Similarly, disconnecting the Device Connector is unrelated to physical PCIe fabric installation and would unnecessarily interrupt Intersight connectivity.

Study Guide Reference: AI Infrastructure Deployment and Data Management — Cisco UCS X-Series GPU expansion, X-Fabric, PCIe connectivity, and hardware deployment.

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VXLAN EVPN provides scalable logical segmentation without requiring physically separate fabrics. Separate VXLAN Network Identifiers can isolate individual AI training projects, while Cisco's Group Policy Option adds a security group identifier to the VXLAN header and enables granular microsegmentation through security-group policies. This makes it possible to isolate tenant or project traffic while selectively allowing access to common services such as centralized storage.

Cisco specifically describes GPO as supporting microsegmentation within VXLAN EVPN fabrics and granular traffic control between application segments. This approach preserves a high-performance shared fabric, avoiding the operational complexity and resource inefficiency of constructing physically separate networks for every project. Cisco also positions VXLAN EVPN as an architecture for AI/ML environments requiring segmentation, secure multitenancy, and scalable high-throughput communication.

Option A provides strong isolation but wastes switching resources and complicates shared-storage connectivity. Option C relies on conventional VLAN boundaries and centralized router ACL enforcement, introducing scaling and potentially additional forwarding constraints. Option D confuses QoS classification with security segmentation; DSCP identifies service classes but is not a security-isolation mechanism.

Study Guide Reference: 2.1 Evaluate network deployment based on bandwidth, latency, scalability, and security.

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The Cisco UCS C885A M8 contains six 3000-W 54-V AC power supplies and normally supports an N+2 power configuration . In the scenario, three PSUs are connected to each independent power grid. Losing one complete grid therefore leaves only three operational 54-V PSUs.

Cisco's installation and service documentation specifies the resulting behavior precisely: when only three 54-V AC PSUs remain available, the server continues operating but enters a reduced-performance condition in which power to each GPU is capped at 60 percent . Cisco further states that four or more 54-V power supplies allow operation at full performance.

Consequently, the system does not immediately shut down, eliminating option A. A graceful operating-system shutdown is also not automatically initiated, eliminating D. Option C is incorrect because three active supplies are insufficient for full-performance operation on this platform.

This behavior allows the server to remain available during a significant power-domain failure while keeping its consumption within the capacity of the remaining PSUs. The operational tradeoff is reduced accelerator performance until adequate power redundancy is restored.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — UCS C885A M8 power redundancy, degraded operation, GPU power management, and availability.

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Cisco Nexus Hyperfabric's full-stack AI infrastructure integrates compute, GPU acceleration, high-performance networking, optional enterprise storage, and centralized cloud-based lifecycle management. Cisco identifies the principal components as the Nexus Hyperfabric cloud controller, Cisco 6000/Nexus switching platforms, Cisco UCS GPU/DPU servers equipped with NVIDIA accelerators, and optional Cisco UCS-based VAST storage.

Option C therefore contains the essential architecture: Cisco UCS servers with NVIDIA GPUs, Cisco switching, optional VAST Data storage, and cloud-managed networking .

Option A omits the cloud-controller architecture and does not specifically identify the NVIDIA-based accelerated compute model that characterizes the full-stack AI solution. Option B incorrectly describes cloud management as optional. Cloud-based fabric design, deployment, visibility, and lifecycle control are fundamental elements of Nexus Hyperfabric. Option D omits GPU compute entirely and reduces the solution to networking and software-defined storage, which does not represent the integrated AI stack.

Cisco's architecture is intended to reduce the complexity associated with constructing AI infrastructure manually. Validated cluster templates coordinate compute, networking, storage, optics, cabling, and lifecycle operations while aligning with NVIDIA enterprise AI reference architectures.

Study Guide Reference: AI Infrastructure Components and Architecture — Cisco Hyperfabric, GPU compute, AI networking, storage integration, scalability, and cloud management.

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The UCS X210c M8 combined with the UCS X580p PCIe Node satisfies the complete requirement set. The X210c M8 uses Intel Xeon 6 processors, while the X580p provides modular GPU expansion and supports up to four PCIe GPUs. Cisco specifically positions this combination for generative AI, inference, and other GPU-accelerated workloads. Management is policy-driven through Cisco Intersight, providing the required cloud-based operational model.

The X580p also supports NVIDIA H200 NVL GPUs and NVLink Bridge capabilities. This is significant where low-latency communication is required between specific GPU pairs because NVLink provides substantially higher-bandwidth GPU-to-GPU communication than standard PCIe-only communication. Cisco's UCS ordering guidance specifies that H200 NVL supports NVL Bridge configurations with a minimum of two GPUs.

Options C and D are high-density AI systems, but they use AMD EPYC-based architectures rather than satisfying the stated Intel CPU requirement. Option A likewise does not match the specified X-Series composability, cloud-managed GPU resource model, and Intel-based requirement as directly as the X210c M8 configuration.

Study Guide Reference: 2.0 AI Infrastructure Components and Architecture — 2.2 Compute deployment; 2.4 power and efficiency considerations.

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A high-performance Cisco UCS architecture normally uses redundant fabric interconnects to provide both high availability and aggregated bandwidth. Cisco documentation describes Fabric Interconnects as typically deployed as a pair, integrating UCS components into a highly available management and connectivity domain. Modern UCS X-Series connectivity can provide up to 200 Gbps aggregate unified-fabric bandwidth per compute node through dual 100-Gbps paths.

Option D therefore aligns with UCS architecture: dual fabrics eliminate the single point of failure associated with one Fabric Interconnect, while 100-Gbps connectivity supports high-throughput AI inference workloads. Port channels on uplinks provide bandwidth aggregation and link-level resiliency toward the upstream data center network.

Option A incorrectly limits the Fabric Interconnect to management traffic; UCS unified fabric carries production Ethernet, storage, and management-related connectivity. Option B bypasses the integrated UCS fabric architecture and unnecessarily separates management onto Catalyst infrastructure. Option C introduces both a single point of failure and a severe bandwidth constraint by using one FI and 10-Gbps links.

For AI inference, where throughput and deterministic latency directly influence application performance, redundant high-speed fabric design is the appropriate implementation.

Study Guide Reference: 3.0 AI Infrastructure Deployment and Data Management — 3.2 Configure high-performance compute and storage using Cisco UCS; 3.2.d LAN connectivity and vNIC policies.

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The uploaded source marks C and D . GPU Memory Utilization in option D is clearly relevant: GPU memory pressure is a critical accelerator metric because model weights, activations, KV caches, tensors, and training state consume GPU-attached memory. Cisco AI monitoring material explicitly includes GPU memory utilization among the telemetry used for AI infrastructure visibility.

Option C, Active CPU Utilization , is useful as a supporting performance metric because AI workloads can become CPU-bound during data preprocessing, tokenization, orchestration, input-pipeline execution, or host-to-device preparation. A CPU bottleneck can therefore leave GPUs underutilized even when sufficient accelerator capacity exists.

However, the question as written has a notable construction issue: Cisco documentation treats GPU utilization itself as a principal metric for evaluating accelerator utilization, but that metric is absent from the available answer choices. Cisco current material explicitly identifies GPU utilization and GPU memory utilization as AI monitoring measurements.

Therefore, CD is retained because it is the answer supplied by the source , but the technically ideal metric pair would be GPU Utilization + GPU Memory Utilization if GPU Utilization were offered.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — GPU utilization, GPU memory utilization, host-resource bottlenecks, and AI performance monitoring.

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Cisco Nexus Dashboard is the appropriate platform for operating and automating Cisco Nexus-based data center fabrics while retaining an NX-OS/CLI-oriented architecture. Nexus Dashboard Fabric Controller provides centralized provisioning, configuration management, automation, templates, monitoring, and lifecycle functions for Nexus fabrics. Cisco explicitly describes NDFC as the comprehensive automation and management platform for Cisco Nexus and MDS infrastructure.

The template requirement is also directly supported. Nexus Dashboard allows policies and configuration templates to be defined centrally and deployed consistently to one or multiple fabrics. For flow visibility, Nexus Dashboard can collect fabric telemetry and reconstruct traffic paths, including packets, drops, anomalies, and flow behavior across multiple switches.

Cisco Catalyst Center—formerly DNA Center—is primarily associated with enterprise campus and access networks rather than Nexus data center AI fabrics. Intersight specializes in compute and infrastructure lifecycle management, particularly UCS. Hyperfabric provides highly automated cloud-managed networking but deliberately abstracts much of the conventional CLI-centric operational model.

Nexus Dashboard therefore best satisfies both requirements: standardized network automation and detailed fabric-flow monitoring while remaining aligned with traditional Nexus operational practices.

Study Guide Reference: AI Infrastructure Deployment and Data Management — Cisco Nexus Dashboard, fabric automation, configuration templates, and telemetry.

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