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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Infrastructure Components and Architecture	30%	- Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability
Topic 2: AI Infrastructure Fundamentals	20%	- Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments
Topic 3: AI Infrastructure Operations and Troubleshooting	20%	- Manage health, logs, and events - Implement performance benchmarking and validation - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues
Topic 4: AI Infrastructure Deployment and Data Management	30%	- Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics

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Cisco Implementing Cisco Data Center AI Infrastructure 認定 300-640 試験問題 (Q17-Q22):

質問 # 17
What does workload distribution offer in an AI infrastructure with local and external resources?

A. distribution of computational tasks across multiple locations
B. restriction of automation to local storage and performance measures
C. isolation of analytic functions within remote processing clusters
D. enforcement of fixed security policies on distribution endpoints

正解：B

解説：
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.

質問 # 18
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?

A. integration with Cisco Nexus Dashboard for advanced network flow analytics and anomaly detection across the data center fabric, which ensures high-speed data transfer between GPU nodes
B. basic server health reporting, which is limited to CPU and memory statistics, and requires specialized third-party tools for any GPU-specific performance monitoring or diagnostics
C. comprehensive health and performance monitoring, which provides real-time telemetry data for UCS servers, including granular metrics for NVIDIA GPUs, which enables direct insights into AI workload execution
D. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts

正解：C

解説：
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.

質問 # 19
What is a function of NVLink technology within a high-performance AI server or cluster?

A. to manage power distribution and thermal regulation across all installed GPU units
B. to provide a high-speed, low-latency direct interconnect for GPU-to-GPU communication
C. to establish a secure, encrypted channel for data transfer between the AI server and external storage
D. to enable remote access and virtualization of GPU resources for distributed AI development teams

正解：B

解説：
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.

質問 # 20
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?

A. iPerf
B. IXIA
C. NCCL
D. Spirent

正解：C

解説：
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.

質問 # 21
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. IGMP
D. ARP

正解：C

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 # 22
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