300-640 Exam Dumps - Cisco CCNP Data Center Questions and Answers

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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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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The PFC pause-frame transmission rate per interface is the most directly relevant congestion indicator for an AI training fabric carrying RoCEv2 traffic. Priority Flow Control protects lossless traffic classes by transmitting pause frames when a receiving interface or switch queue approaches a congestion threshold. An elevated or sustained PFC pause rate therefore indicates that the lossless RDMA traffic class is repeatedly encountering queue pressure.

This is particularly significant for distributed AI training. GPU collective operations require synchronized communication between large numbers of accelerators, and excessive congestion can create tail latency, reduce effective network throughput, and extend Job Completion Time. Cisco AI networking guidance consequently treats PFC, ECN, queue occupancy, drops, and congestion telemetry as key operational signals for AI backend fabrics.

Spanning-tree topology changes are largely irrelevant in a modern routed or VXLAN-EVPN AI backend. BGP EVPN route counts are useful for control-plane capacity assessment but do not directly reveal congestion. VXLAN encapsulation rates measure traffic volume rather than whether buffers are becoming saturated.

PFC should not be interpreted as inherently problematic—some pause activity may occur during microbursts. However, unusually high or persistent pause-frame rates are a strong signal that congestion behavior requires investigation.

Study Guide Reference: AI Infrastructure Operations and Troubleshooting — RoCEv2 monitoring, PFC, congestion, telemetry, and AI training performance.

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When an AI environment contains both local and external resources, workload distribution enables computational jobs to be placed or divided across multiple locations according to performance, capacity, latency, data residency, security, and cost requirements. This is a defining capability of hybrid AI infrastructure rather than an isolation mechanism.

Cisco defines hybrid AI infrastructure as combining on-premises and cloud resources within a common operating model. Workloads can use cloud capacity for burst training or experimentation while sensitive data or steady inference operations remain on premises. Cisco further identifies workload mobility, secure connectivity, and data synchronization as explicit requirements for hybrid AI deployment in DCAI objective 2.5. Cisco material on AI workload orchestration likewise describes workload execution across on-premises, cloud, and hybrid environments.

Option A unnecessarily isolates functions rather than distributing them. Option C contradicts hybrid automation by restricting operations locally. Option D describes security-policy enforcement, which is important but does not define workload distribution.

Accordingly, B is the technically correct response. It also corrects the answer indicated in the supplied source document, which lists C despite Cisco's current hybrid-infrastructure guidance.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.4.b Hybrid; 2.5 Hybrid AI deployment and workload mobility.

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Cisco AI PODs provide a prevalidated, integrated full-stack infrastructure platform for enterprise artificial intelligence. Cisco describes AI PODs as combining Cisco UCS compute, Cisco Nexus networking, accelerated GPUs, enterprise storage, management, orchestration, and supporting software into an architecture designed for the complete AI workload lifecycle. The supported workload spectrum explicitly includes model training, fine-tuning, and high-throughput inference.

Option D therefore captures both the architectural scope and workload intent. The principal value of AI POD is that organizations do not need to independently design and validate every server, accelerator, network, storage, and software dependency. Cisco supplies validated designs and modular scale-unit architectures that accelerate deployment while retaining enterprise operational characteristics.

Option A is too restrictive because AI POD is not merely cloud-native lifecycle software and its hardware scope extends far beyond compute. Option B incorrectly makes inferencing the primary focus; Cisco AI POD supports the complete AI lifecycle and can be purpose-built for training, inference, or mixed workloads. Option C understates the architecture by describing it merely as modular hardware components. Cisco explicitly characterizes AI POD as an integrated full-stack solution.

Study Guide Reference: AI Infrastructure Components and Architecture — Cisco AI infrastructure solutions, compute, networking, storage, GPU acceleration, and scalable architectures.

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An IP-based shared storage array is the appropriate architecture for a RAG environment containing approximately 50 TB of enterprise documents. RAG requires compute nodes to access a common body of source documents, embeddings, vector indexes, model artifacts, and intermediate datasets. With six UCS X210c servers participating in the solution, centralized shared storage avoids creating isolated data silos on individual compute nodes and allows the full AI cluster to access the same authoritative dataset.

Cisco AI POD and RAG architectures use enterprise shared storage connected through high-speed Ethernet. Cisco validates IP-based protocols including NFS, iSCSI, NVMe/TCP, and—in higher-performance AI platforms—RDMA-capable storage designs. Cisco's RAG architecture with FlexPod, for example, uses NFS, iSCSI, and NVMe/TCP storage networks, while Cisco AI PODs support external storage from partners such as NetApp, Pure Storage, and VAST Data.

JBOD and RAID 5 local storage remain tied to individual servers and do not provide the desired shared data layer. Tape provides economical archival retention but lacks the random-access latency and throughput required for active RAG retrieval.

Study Guide Reference: AI Infrastructure Components and Architecture — storage selection, AI data access, scalability, RAG infrastructure, and shared storage.

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Rapid provisioning supports scalability by reducing the time required to make compute, GPU, networking, storage, and orchestration resources available for workloads. The most appropriate description is therefore the efficient launch of computational platforms . When new AI capacity can be provisioned consistently and quickly, organizations can expand training environments, increase inference capacity, respond to workload bursts, and introduce additional accelerator resources without lengthy manual configuration.

Cisco's DCAI objectives explicitly cover orchestration as a component of AI environments and require candidates to evaluate infrastructure scalability. Cisco also emphasizes that AI infrastructure must scale to accommodate changing workloads and that cloud-oriented infrastructure offers rapid access to computational resources when demand changes.

Option A introduces fragmented management, which generally increases operational complexity. Option B restricts connectivity and therefore conflicts with scalable distributed AI execution. Option C explicitly delays resource availability and is the opposite of rapid provisioning.

The architectural objective is to make infrastructure consumption repeatable and automated. Efficient provisioning allows the underlying platform to expand at the pace demanded by GPU-intensive training and production inference rather than becoming an operational bottleneck.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.5.d Orchestration; AI infrastructure scalability concepts.

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