Implementing Cisco Data Center AI Infrastructure 300-640 guide torrent materials
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Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI and machine learning concepts - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio |
| Topic 2: AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability |
| Topic 3: AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration |
| Topic 4: AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
1. A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
A) Reduced need for IP addressing
B) Elimination of routing protocols
C) Simplified wireless integration
D) Predictable low-latency east-west traffic
2. A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)
A) Monitor
B) System CPU Utilization
C) GPU Performance
D) Explorer
E) GPU Utilization
3. Refer to the exhibit. Users report delayed response times and network latency when running certain AI workloads on a specific Cisco UCS Intersight Managed Mode Domain. Based on the fault message and reported symptoms, which troubleshooting action must be taken first to address the network latency issues?
A) Review the pause frame counters for signs of congestion on Fabric Interconnect B Port 11.
B) Check the physical cabling and SFP+ transceivers on port 11 of switch B for potential hardware failures or incorrect configurations.
C) Examine the UCS fabric interconnect configuration for port-channel 11 on Fabric Interconnect B.
D) Review the AI workload configuration to optimize resource allocation (CPU, memory, GPU) for the affected applications.
4. A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?
A) Equal-cost multipath routing
B) Layer 2 loop dependency
C) Static route redistribution
D) Single-path forwarding
5. How does RAG enhance the capabilities of LLMs?
A) by retaining the LLM by altering its core parameters
B) by removing inherent biases from LLM outputs
C) by removing the data preparation and cleaning step
D) by providing access to proprietary data
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C,E | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |



