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Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Manage health, logs, and events - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues |
| AI Infrastructure Fundamentals | 20% | - 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 - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments |
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
1. 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) Active CPU Utilization
C) Explorer
D) Insights
E) GPU Memory Utilization
2. 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?
A) Cisco UCS C240 Servers
B) Cisco Unified Edge
C) Cisco UCS X-Series Direct
D) Cisco UCS C845A M8 Server
3. An organization deploys Cisco Nexus switches supporting RoCEv2 traffic. Which QoS behavior is most critical to maintain reliable GPU communication?
A) Randomized path selection
B) Best-effort forwarding
C) Strict packet fragmentation
D) Low-latency lossless transport
4. During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?
A) NetFlow with hourly exports
B) Manual CLI inspection once per week
C) Daily syslog review
D) Streaming telemetry with high-frequency sampling
5. An engineer configures quality of service in the Cisco ACI fabric to connect VAST storage servers. Which combination of attributes must be selected?
A) Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Enabled
B) Congestion Algorithm: Tail Drop
Congestion Notification: Disabled
C) Congestion Algorithm: Tail Drop
Congestion Notification: Enabled
D) Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Disabled
Solutions:
| Question # 1 Answer: B,E | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: A |
