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Cisco 300-640 Exam Syllabus Topics:

Section	Objectives
Topic 1: Security and Governance in AI Data Centers	- Infrastructure security 1. Network segmentation and policy enforcement 2. Secure access to AI workloads
Topic 2: AI Infrastructure Deployment and Operations	- Operations and lifecycle management 1. Performance tuning and optimization 2. Monitoring and observability - Deployment models 1. On-premises AI clusters 2. Hybrid AI infrastructure integration
Topic 3: 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
Topic 4: 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 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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最新的 CCNP Data Center 300-640 免費考試真題 (Q63-Q68):

問題 #63
How does RAG enhance the capabilities of LLMs?

A. by providing access to proprietary data
B. by removing the data preparation and cleaning step
C. by removing inherent biases from LLM outputs
D. by retaining the LLM by altering its core parameters

答案：A

解題說明：
RAG enhances LLMs by retrieving relevant external or proprietary information at generation time and using that context to produce more accurate, current, and domain-specific responses without changing the model's core parameters.

問題 #64
An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?

A. VAST Data Universal Storage
B. Pure Storage FlashBlade
C. Local NVMe SSDs within each GPU server
D. NetApp All-Flash FAS (AFF)

答案：A

解題說明：
VAST Data Universal Storage provides a software-defined, disaggregated shared-everything storage architecture designed for AI-scale workloads. It supports Cisco UCS-based deployments managed through Intersight and delivers high-performance all-flash storage with RDMA and GPU Direct Storage capabilities for large-scale AI training, fine-tuning, and inference environments.

問題 #65
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?

A. C245 M8
B. C845A M8
C. C885A M8
D. X210C M8 with x580 PCIe node

答案：D

解題說明：
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

問題 #66
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?

A. Single-path forwarding
B. Equal-cost multipath routing
C. Layer 2 loop dependency
D. Static route redistribution

答案：B

解題說明：
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.

問題 #67
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. Strict packet fragmentation
C. Randomized path selection
D. Low-latency lossless transport

答案：D

解題說明：
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.

問題 #68
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