300-610 Exam Dumps : Designing Cisco Data Center Infrastructure for Traditional and AI Workloads v1.2 (DCID 300-610)

The collapsed-core topology is the simplest of the listed SAN architectures because it removes a separate switching tier. Hosts and storage systems connect to the same high-port-density director or core-class switches, so traffic does not traverse dedicated edge switches and additional inter-switch links before reaching the storage target. Cisco’s SAN topology guidance describes collapsed core as simple to architect and implement and straightforward to maintain and troubleshoot. Fewer switches, ISLs, and fabric hops reduce configuration dependencies, failure points, zoning-path complexity, and the number of devices that must be inspected during fault isolation. The design also uses ports efficiently because interfaces that would otherwise form an edge-to-core layer can connect end devices directly. This simplicity has a scaling boundary: as the number of hosts and storage ports grows, a core-edge design may provide better expansion and aggregation, and physically separate fabrics A and B are still required for high availability. A core-edge topology adds an edge tier and ISLs, improving large-scale expansion but increasing operational complexity. A mesh interconnect creates many switch-to-switch relationships and becomes difficult to scale and troubleshoot. Edge-core-edge introduces still more tiers and is appropriate only when scale or traffic-engineering requirements justify them. Because the question asks specifically for the topology characterized by uncomplicated architecture, implementation, maintenance, and troubleshooting—not maximum scale—the collapsed-core design is the exact match. Cisco MDS collapsed-core deployment guidance

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GPU-based compute connected by a high-throughput, low-latency network best satisfies the combined training and inference requirements. Model training performs large numbers of parallel matrix and tensor operations, which GPUs execute far more efficiently than general-purpose CPUs. Frequent retraining also requires rapid movement of training data between storage, host memory, accelerators, and—when training is distributed—between GPUs on different servers. High-throughput networking prevents the accelerators from sitting idle while gradients, parameters, or data batches are exchanged. Real-time fraud inference adds a latency requirement: transaction features must reach the model quickly and predictions must return within the application’s response window. Cisco’s AI/ML data-center blueprint emphasizes predictable, high-performance networking for accelerator clusters, while Cisco UCS GPU platforms are designed for high-throughput inference with minimized latency. CPU-only instances may handle lightweight inference but are inefficient for frequent large-scale training. Local SSDs improve data access but do not provide the required inter-compute fabric. Generic cloud elasticity can add capacity, yet Option C does not guarantee GPU acceleration or the deterministic low-latency east-west connectivity stated in the question. Dedicated multicore CPUs and distributed storage remain limited by CPU parallelism and network overhead. Option B directly couples the correct accelerator architecture with the fabric characteristics needed for scalable training and immediate inference, making it the complete solution. Cisco AI/ML data-center networking blueprint

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