100% Pass 2026 Cisco Unparalleled 300-640: Implementing Cisco Data Center AI Infrastructure Latest Learning Materials

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Cisco 300-640 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 2: 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
              Topic 3: Compute and Storage for AI Infrastructure- Storage systems
              • 1. Distributed storage concepts
                • 2. High-throughput storage for AI training data
                  - Compute architecture
                  • 1. HPC-style scaling considerations
                    • 2. GPU-based compute nodes
                      Topic 4: AI Infrastructure Deployment and Operations- Operations and lifecycle management
                      • 1. Performance tuning and optimization
                        • 2. Monitoring and observability
                          - Deployment models
                          • 1. Hybrid AI infrastructure integration
                            • 2. On-premises AI clusters
                              Topic 5: Security and Governance in AI Data Centers- Infrastructure security
                              • 1. Secure access to AI workloads
                                • 2. Network segmentation and policy enforcement

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                                  Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q60-Q65):

                                  NEW QUESTION # 60
                                  A data center team enables jumbo frames in a Cisco AI cluster. What is the primary benefit of increasing the MTU for east-west GPU traffic?

                                  Answer: C

                                  Explanation:
                                  Jumbo frames reduce the number of packets needed to transmit large AI datasets, decreasing CPU interrupts and protocol overhead. This improves throughput and efficiency for GPU communication. Larger MTUs do not directly affect convergence or eliminate buffering, and they typically reduce rather than increase processing overhead.


                                  NEW QUESTION # 61
                                  A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment. Which Cisco Intersight capability is most critical for effectively monitoring this AI- centric infrastructure?

                                  Answer: D

                                  Explanation:
                                  Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.


                                  NEW QUESTION # 62
                                  A network architect wants to provide high availability for AI compute nodes connected to dual Cisco Nexus switches. Which technology enables active-active uplink forwarding without STP blocking?

                                  Answer: C

                                  Explanation:
                                  Virtual Port Channel (vPC) allows devices to use active-active uplinks across two Nexus switches while avoiding spanning-tree blocking. This increases bandwidth utilization and resiliency for AI clusters. GLBP and VRRP provide gateway redundancy, while PVST+ still relies on blocking redundant paths.


                                  NEW QUESTION # 63
                                  What is a purpose of Cisco AI PODs?

                                  Answer: A

                                  Explanation:
                                  Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.


                                  NEW QUESTION # 64
                                  A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?

                                  Answer: D

                                  Explanation:
                                  Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.


                                  NEW QUESTION # 65
                                  ......

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