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Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability - Manage health, logs, and events |
| AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Integrate AI frameworks and applications with Cisco infrastructure - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
| AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio |
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
1. What is the process involved in workload scheduling for AI environments?
A) generation of backup images for restoration operations
B) optimization of licensing structures for compliance
C) assignment of tasks to compute elements for optimized performance
D) aggregation of device statistics for reporting
2. A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?
A) UCS C-series Standalone
B) Unified Edge
C) UCS X-series
D) UCS M-series
3. 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) Simplified wireless integration
B) Reduced need for IP addressing
C) Elimination of routing protocols
D) Predictable low-latency east-west traffic
4. Drag and Drop Question
A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.
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: Tail Drop
Congestion Notification: Disabled
B) Congestion Algorithm: Tail Drop
Congestion Notification: Enabled
C) Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Disabled
D) Congestion Algorithm: Weighted Random Early Detection (WRED)
Congestion Notification: Enabled
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: Only visible for members | Question # 5 Answer: D |


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