Quiz 2026 Valid Cisco 300-640: Implementing Cisco Data Center AI Infrastructure Latest Mock Exam

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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Infrastructure Deployment and Operations- Deployment models
  • 1. Hybrid AI infrastructure integration
    • 2. On-premises AI clusters
      - Operations and lifecycle management
      • 1. Monitoring and observability
        • 2. Performance tuning and optimization
          Topic 2: Security and Governance in AI Data Centers- Infrastructure security
          • 1. Network segmentation and policy enforcement
            • 2. Secure access to AI workloads
              Topic 3: Data Center AI Infrastructure Fundamentals- AI/ML workload requirements in data centers
              • 1. Compute, GPU, and acceleration concepts
                • 2. Data pipeline and workload characteristics
                  Topic 4: Compute and Storage for AI Infrastructure- Compute architecture
                  • 1. GPU-based compute nodes
                    • 2. HPC-style scaling considerations
                      - Storage systems
                      • 1. Distributed storage concepts
                        • 2. High-throughput storage for AI training data
                          Topic 5: Data Center Networking for AI Workloads- Cisco Data Center networking technologies
                          • 1. Cisco ACI integration concepts
                            • 2. Cisco Nexus platform concepts
                              - High-performance fabric design
                              • 1. Leaf-spine architecture in AI environments
                                • 2. Low-latency switching and routing considerations

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q51-Q56):

                                  NEW QUESTION # 51
                                  Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?

                                  Answer: A

                                  Explanation:
                                  A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.


                                  NEW QUESTION # 52
                                  A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?

                                  Answer: B

                                  Explanation:
                                  Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.


                                  NEW QUESTION # 53
                                  A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?

                                  Answer: B

                                  Explanation:
                                  Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.


                                  NEW QUESTION # 54
                                  A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?

                                  Answer: C

                                  Explanation:
                                  AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.


                                  NEW QUESTION # 55
                                  A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
                                  - policy managed through cloud-based interface
                                  - low latency between specific pairs of GPUs
                                  - at least 2 GPUs per server
                                  - all GPUs should be same model
                                  - Intel CPU
                                  - well suited for Inferencing
                                  - customer is power conscious
                                  Which server meets the requirements?

                                  Answer: D

                                  Explanation:
                                  The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
                                  Reference:
                                  https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
                                  https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf


                                  NEW QUESTION # 56
                                  ......

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