300-640対応内容 & 300-640試験情報

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Cisco 300-640 Exam Syllabus Topics:

Section	Objectives
Topic 1: Security and Governance in AI Data Centers	- Infrastructure security 1. Secure access to AI workloads 2. Network segmentation and policy enforcement
Topic 2: Compute and Storage for AI Infrastructure	- Storage systems 1. High-throughput storage for AI training data 2. Distributed storage concepts - Compute architecture 1. HPC-style scaling considerations 2. GPU-based compute nodes
Topic 3: 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 4: Data Center AI Infrastructure Fundamentals	- AI/ML workload requirements in data centers 1. Data pipeline and workload characteristics 2. Compute, GPU, and acceleration concepts
Topic 5: 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 ACI integration concepts 2. Cisco Nexus platform concepts

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Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q16-Q21):

質問 # 16
What is a purpose of Cisco AI PODs?

A. to provide a turnkey, full-stack infrastructure for AI training, fine-tuning, and inferencing
B. to offer a flexible AI infrastructure focused on inferencing workloads with optional training capabilities
C. to enable AI lifecycle management through cloud-native software with hardware support limited to compute servers
D. to deliver modular AI hardware components that can be integrated with third-party software platforms

正解：A

解説：
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.

質問 # 17
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?

A. CDP
B. LLDP
C. ARP
D. IGMP

正解：D

解説：
Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.

質問 # 18
An engineer deploys an AI fabric on Cisco Nexus 9000 Series Switches connected to Cisco UCS nodes with NVIDIA GPUs. The requirements call for optimal network performance for training workloads. Adaptive routing is enabled on the NICs, and per-packet load balancing is enabled on the switches. Which other configuration must be implemented for this integration to be fully operational?

A. Include switch configuration "hardware profile spectrum-x" in global configuration mode.
B. Include matching statements in your QoS policy so the switch knows which incoming packets to load balance.
C. Add the "adaptive-routing" keyword under the switch load balancing configuration.
D. Set the cisco_dlb=1 option in the NIC configuration so the NIC knows it can use adaptive routing.

正解：D

解説：
The NIC must be explicitly configured with the Cisco dynamic load-balancing setting so it recognizes that the connected Cisco Nexus fabric supports adaptive routing. This completes the integration between NIC-side adaptive routing and switch-side per-packet load balancing for optimized AI training traffic.

質問 # 19
An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?

A. Best-effort forwarding
B. Randomized path selection
C. Low-latency lossless transport
D. Strict packet fragmentation

正解：C

解説：
RoCEv2 depends on low-latency and near-lossless Ethernet transport to maintain efficient RDMA communication. Packet drops trigger retransmissions and significantly degrade AI training performance. Best-effort forwarding cannot guarantee delivery quality, while fragmentation and randomized path selection do not address the primary RDMA requirement.

質問 # 20
A customer uses Cisco AI Products within Intersight and observes packet drops for RoCEv2 traffic. Which System QoS policy configuration must be deployed to ensure that RoCEv2 traffic is classified as lossless?

A. Enable all QoS system classes.
B. Disable Platinum system class.
C. Disable Gold system class.
D. Enable Platinum class and uncheck Allow Packet Drops.

正解：D

解説：
RoCEv2 traffic must be mapped to a lossless system class. Enabling the Platinum class and clearing Allow Packet Drops configures that class as lossless, preventing packet drops for RoCEv2 traffic and supporting reliable RDMA communication for AI workloads.

質問 # 21
......

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