Exam 300-640 Vce | 300-640 Detailed Study Plan

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Cisco 300-640 Exam Syllabus Topics:

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

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

                                  NEW QUESTION # 51
                                  What is the process involved in workload scheduling for AI environments?

                                  Answer: D

                                  Explanation:
                                  Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.


                                  NEW QUESTION # 52
                                  An engineer configures Cisco Nexus Dashboard Fabric Controller for AI infrastructure automation. The company wants centralized policy management and simplified fabric provisioning across multiple pods. Which capability provides this benefit?

                                  Answer: C

                                  Explanation:
                                  Intent-based fabric automation simplifies deployment and policy consistency across large AI infrastructures. Nexus Dashboard Fabric Controller automates provisioning, validation, and operational management. Distributed ACLs and local VLAN switching are operational features but do not centralize orchestration. Manual CLI verification is error-prone and unsuitable for rapidly scaling AI environments.


                                  NEW QUESTION # 53
                                  Which components are part of the HyperFabric AI solution?

                                  Answer: C

                                  Explanation:
                                  HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.


                                  NEW QUESTION # 54
                                  Drag and Drop Question
                                  A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.

                                  Answer:

                                  Explanation:


                                  NEW QUESTION # 55
                                  A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

                                  Answer: C

                                  Explanation:
                                  RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.


                                  NEW QUESTION # 56
                                  ......

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