Cisco 300-610 Exam With Confidence Using Practice Dumps

Cisco ACI Policy-Based Redirect is designed for precisely this requirement: the ACI fabric remains the servers’ default gateway, while a contract service graph redirects selected traffic to an L4-L7 appliance. For routed PBR, Cisco specifies that the fabric must be the gateway for both the servers and the redirect node, and that the L4-L7 device operates in go-to, or routed, mode. The leaf applies the redirect policy to the matching Server 1-to-Server 2 flow, sends it to the load balancer attached at S3, and resumes normal fabric forwarding after the appliance processes it. This avoids changing endpoint default gateways or adding application-specific routes, and it allows the fabric to select the direct path to the service node, minimizing unnecessary hops. Option B moves the gateway outside ACI and a one-arm load balancer generally requires source NAT or PBR to preserve symmetric return traffic. Option C also moves the gateway outside the fabric, contradicting the documented ACI PBR model and sacrificing distributed anycast routing. A two-arm load balancer can be integrated with ACI, but Option D omits the redirect mechanism; simply attaching a two-arm device does not force selected traffic through it when ACI remains the gateway. The go-to PBR service graph is therefore the complete policy-based solution that provides deterministic service insertion without altering the existing routing table. Cisco ACI PBR service-graph design

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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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