300-640 Implementing Cisco Data Center AI Infrastructure Pass4sure Zertifizierung & Implementing Cisco Data Center AI Infrastructure zuverlässige Prüfung Übung

Viele der ExamFragen 300-640 Implementing Cisco Data Center AI Infrastructure Prüfungsvorbereitung Antworten sind in Vielfache-Wahl-Fragen (MCQs) FormatQualität geprüften Implementing Cisco Data Center AI Infrastructure Produkte viele Male vor der VeröffentlichungKostenlose Demo der Prüfung ExamFragen 300-640 an ExamFragen. Um Ihre Zertifizierungsprüfungen reibungslos erfolgreich zu meistern brauchen Sie nur unsere Prüfungsfragen und Antworten zu Cisco 300-640 (Implementing Cisco Data Center AI Infrastructure)auswendigzulernen.

Cisco 300-640 Exam Syllabus Topics:

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

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                                  Cisco Implementing Cisco Data Center AI Infrastructure 300-640 Prüfungsfragen mit Lösungen (Q46-Q51):

                                  46. Frage
                                  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?

                                  Antwort: B

                                  Begründung:
                                  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.


                                  47. Frage
                                  What describes inference traffic patterns in AI deployments?

                                  Antwort: A

                                  Begründung:
                                  Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.


                                  48. Frage
                                  A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
                                  - high availability of the chassis management plane.
                                  - support for up to 768 GB of memory for in-memory model storage and
                                  processing
                                  - remote server launch requiring no on-site IT staff
                                  - use of Cisco Intersight for SaaS-based infrastructure lifecycle
                                  management
                                  Which solution meets the requirements?

                                  Antwort: C

                                  Begründung:
                                  Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.


                                  49. Frage
                                  A data center facility experienced a power outage, and the UCS X-Series servers did not restart automatically when power is restored. The configuration must be updated to prioritize these servers to boot as soon as possible to resume their work. Which set of power policy configurations must be used to meet these requirements?

                                  Antwort: D

                                  Begründung:
                                  The servers must be configured to power back on automatically after power is restored, so the restore behavior should be set to always power on. Setting the power priority to high ensures these UCS X-Series servers receive priority during power recovery, allowing them to resume workloads as quickly as possible.


                                  50. Frage
                                  A Cisco Nexus switch participates in an AI cluster using ECMP forwarding. Several flows between GPU servers consistently use the same uplink, creating congestion while other links remain underutilized. Which action improves traffic distribution?

                                  Antwort: B

                                  Begründung:
                                  ECMP relies on hashing algorithms to distribute traffic across equal-cost paths. In AI environments, large elephant flows may hash to the same uplink, causing imbalance. Adjusting the hashing method improves entropy and distributes flows more evenly. Increasing MTU improves efficiency but does not rebalance traffic. Port security and disabling hashing are unrelated to load distribution.


                                  51. Frage
                                  ......

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