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hardware profile dlb

dlb-interface all

mode flowlet

The exhibit shows materially uneven utilization across the eight interfaces, with receive utilization ranging from approximately 32.5% to 75.3%. Cisco Dynamic Load Balancing addresses this problem by considering current path utilization rather than relying exclusively on static ECMP hashing.

The configuration begins with hardware profile dlb, which enters the DLB hardware-profile context. dlb-interface all enables DLB on all eligible Layer 3 physical interfaces. Cisco documents both an explicit interface list and the all keyword for this purpose. The document's supplied drag-and-drop answer then selects mode flowlet. Cisco defines flowlet load balancing as the default DLB mode, in which forwarding decisions use Dynamic Resilient Ethernet metrics to select less-loaded paths while maintaining packet ordering within a flowlet.

Cisco also supports per-packet DLB, which can produce even greater path utilization but can reorder packets. When NVIDIA adaptive-routing capabilities are explicitly enabled and endpoints can handle reordering, Cisco documents per-packet DLB as an optimized alternative. That additional prerequisite is not stated in this question.

Therefore, the source-derived configuration is DLB / all interfaces / flowlet .

Study Guide Reference: AI Infrastructure Deployment and Data Management — Dynamic Load Balancing, ECMP optimization, AI backend fabrics, and traffic distribution.

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Cisco UCS X-Series is the strongest fit because it is designed as a modular, scalable compute platform that can support conventional enterprise workloads and GPU-accelerated AI workloads within the same architectural framework. Cisco explicitly positions UCS X-Series for workload consolidation, allowing compute nodes and GPU resources to be added as requirements evolve without replacing the entire infrastructure. The platform supports substantial memory capacity, virtualization, GPU acceleration, and centralized lifecycle management through Cisco Intersight.

The current UCS X-Series portfolio supports X210c and X215c compute nodes, X580p GPU expansion, and X-Fabric connectivity. Cisco further positions the platform for AI workloads through high-bandwidth, low-latency GPU connectivity and scalable GPU and CPU configurations.

UCS M-Series is not the current strategic modular platform for these requirements. Unified Edge is optimized for distributed edge deployments rather than a large, expanding enterprise data center. A standalone C-Series deployment can provide powerful GPU servers, but it does not offer the same chassis-level composability and resource expansion model described in the scenario.

Study Guide Reference: 2.0 AI Infrastructure Components and Architecture — 2.2 Evaluate compute deployment based on CPU, GPU, memory, virtualization, scalability, redundancy, and workload requirements.

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A rail-optimized architecture is intentionally designed so that communication among corresponding GPU ranks within a single scalable unit can remain on the rail or leaf switches. This provides single-hop forwarding and avoids unnecessary traversal through spine switches, reducing latency for collective communication.

Spines become necessary when the deployment expands beyond one scalable unit. Cisco explicitly documents that multiple scalable units can be interconnected through leaf-and-spine switching. Traffic among GPUs inside one scalable unit benefits from rail-optimized single-hop communication, whereas traffic between GPUs located in different scalable units traverses the spine layer.

Therefore, option C precisely describes when spine switches provide architectural value: scaling the AI fabric beyond the boundaries of one scalable unit .

Option A is inaccurate because the rail design already provides the required local GPU bandwidth within the scalable unit; adding spines is not primarily intended to increase local rail bandwidth. Option B mischaracterizes the principal design purpose. Although additional topology can contribute to resiliency, spines are introduced fundamentally for interconnecting scalable units and enabling scale-out. Option D is also incorrect because increased buffering is not the architectural reason for introducing a spine tier.

Study Guide Reference: AI Infrastructure Components and Architecture — rail-optimized topology, scalable units, nonblocking network design, latency, and horizontal scaling.

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Cisco Nexus 9000 integration with NVIDIA adaptive-routing capabilities requires the Nexus switch to enable the Spectrum-X hardware profile . Cisco's current NX-OS configuration guide explicitly specifies the global configuration command hardware profile spectrum-x to enable NVIDIA Spectrum-X adaptive routing. When a Nexus switch peers with a supported NVIDIA NIC, this capability allows the NIC to handle packet reordering created by granular techniques such as per-packet Dynamic Load Balancing.

This is particularly important for AI training because large synchronized RDMA flows can create uneven utilization under conventional hash-based ECMP. Cisco explains that per-packet DLB can spray packets over multiple paths and that NVIDIA adaptive routing/direct data placement enables the receiver to correctly handle packets that arrive out of order, improving utilization and collective-operation performance.

Option A is not the documented NX-OS configuration syntax. Option B is not the Cisco-prescribed Nexus integration command. Option D may be relevant to traffic classification in other designs but does not activate Spectrum-X adaptive-routing interoperability.

Therefore, the Cisco-documented configuration is C. This differs from the supplied file's marked answer B.

Study Guide Reference: 3.0 AI Infrastructure Deployment and Data Management — 3.1.d Load distribution.

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The surveillance workload creates a substantial persistent-capacity requirement across every location. Even at the stated minimum of ten cameras, each store generates approximately 240 GB per day and roughly 3.36 TB over fourteen days before considering filesystem overhead, redundancy, additional cameras, or growth. A scalable IP-based shared-storage architecture is therefore the appropriate option.

Cisco Unified Edge is designed around integrated high-speed Ethernet networking and supports network-accessible storage through standard IP mechanisms. Cisco's validated Unified Edge guidance describes network storage using protocols such as NFS and iSCSI , while explicitly noting that Fibre Channel is not validated in that design. Unified Edge also supports software-defined and distributed storage architectures carried over its Ethernet infrastructure.

Fibre Channel would introduce specialized SAN adapters, switches, cabling, and operational complexity that are generally inappropriate for a cost-sensitive, highly distributed retail edge. InfiniBand is optimized for specialized high-performance computing rather than economical video retention. Local storage could hold surveillance data at each store, but the question explicitly requires a centralized storage solution and long-term capacity that can be managed across locations.

IP-based shared storage therefore provides the best combination of scalability, standardization, cost efficiency, and Unified Edge compatibility.

Study Guide Reference: AI Infrastructure Components and Architecture — edge AI storage, IP storage, scalability, capacity, and deployment requirements.

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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 with NVIDIA H200 GPUs is purpose-built for dense accelerated computing and directly satisfies the NVLink requirement. Cisco's C885A M8 NVIDIA configuration supports eight H100 or H200 SXM Tensor Core GPUs on an HGX platform. Those GPUs are interconnected using NVIDIA NVLink, providing the high-bandwidth GPU-to-GPU communication necessary for distributed tensor processing inside the server.

The platform also provides substantial host-memory capacity, dual AMD EPYC processors, high-speed local NVMe storage, multiple 400-GbE adapters for inter-node GPU communication, and Cisco Intersight integration. These capabilities make it appropriate for large language models, generative AI training, fine-tuning, RAG, and large inference workloads.

Option A does not provide the same HGX NVLink-based eight-GPU architecture with an L40 configuration. Option B uses AMD accelerators; AMD GPU connectivity employs AMD interconnect technologies rather than NVIDIA NVLink. Option C is designed for materially different workload and GPU-density requirements and does not deliver the HGX H200 NVLink topology specified by the scenario.

The critical selection criterion is not merely whether a server can host a discrete GPU. The question explicitly requires direct GPU-to-GPU access through NVLink , which identifies the NVIDIA HGX-based C885A M8 configuration.

Study Guide Reference: AI Infrastructure Components and Architecture — compute selection based on CPU, GPU, memory, virtualization, interconnect performance, and workload requirements.

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Retrieval-Augmented Generation enhances an LLM by retrieving relevant information from external knowledge sources and incorporating that material into the context supplied to the model during generation. This permits a pretrained model to answer questions using organization-specific information without requiring its underlying model parameters to be retrained for every knowledge update.

Cisco describes RAG as a technique that connects LLM applications to additional data sources, including internal repositories containing sensitive customer or business information. Cisco's enterprise RAG guidance specifically emphasizes using proprietary organizational data to ground responses in enterprise information. Therefore, providing access to proprietary data is the correct answer.

Option A is incorrect because RAG does not eliminate ingestion, parsing, chunking, cleaning, embedding, or indexing requirements. Option B is too absolute: grounding can improve factual relevance but does not inherently remove model bias. Option D describes modification of model parameters, which is characteristic of training or fine-tuning rather than standard RAG retrieval.

Cisco explicitly includes RAG as a required DCAI workload type, so candidates must distinguish retrieval-based augmentation from model training and fine-tuning.

Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.1.a RAG.

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In Cisco Intersight Managed Mode, Fabric Interconnect physical port roles and port-channel definitions are configured through the Port Policy . Cisco's current Intersight configuration documentation states that a Port Policy configures ports and port roles on a Fabric Interconnect and permits administrators to enable or disable individual ports or port channels. Supported roles include Ethernet uplinks and Ethernet uplink port channels.

The Port Policy is subsequently associated with the UCS Domain Profile. Cisco documents that a Domain Profile configures an FI pair through reusable policies and controls ports, port channels, VLANs, and VSANs. During Domain Profile creation, the administrator attaches the appropriate Port Policy to each Fabric Interconnect.

A LAN Connectivity Policy is used primarily at the server-profile level to define vNIC connectivity. A Network Connectivity Policy configures DNS-related server-management settings rather than FI uplink topology. An Ethernet Network Group Policy defines VLAN groups associated with Ethernet connectivity and can be referenced by uplink configurations, but it does not itself create the FI port channel.

Consequently, the policy that must be changed to build the multiple-100-GbE uplink port channel is the Port Policy .

Study Guide Reference: AI Infrastructure Deployment and Data Management — Intersight Managed Mode, UCS Domain Profiles, Fabric Interconnect port configuration, and uplink resiliency.

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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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