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Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures |
| Topic 3: AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events - Troubleshoot network, compute, storage, and orchestration issues |
| Topic 4: 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 - 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 |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
1. Drag and Drop Question
Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.
2. A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?
A) Priority Flow Control
B) Storm Control
C) DHCP Snooping
D) BPDU Guard
3. Which function is provided through natural language processing?
A) annotation of data transmission protocols for device authorization
B) compression of large configuration files for storage efficiency
C) automated conversion of spoken or written content into structured data
D) filtration of duplicate entries from transaction records
4. A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?
A) Cisco Intersight AI fabric with Nexus 9000 switches and AI servers on premises
B) Cisco Nexus Dashboard deployed in AWS or Azure with Nexus 9000 switches and AI servers on premises
C) Cisco HyperFabric AI Medium AI Cluster with Cisco 6000 switches and AI servers on premises
D) Cisco HyperFabric AI Small AI Cluster with Cisco 6000 switches and AI servers on premises
5. A data center team enables jumbo frames in a Cisco AI cluster. What is the primary benefit of increasing the MTU for east-west GPU traffic?
A) Increased encryption overhead
B) Reduced Layer 3 convergence time
C) Lower CPU utilization and fewer frames
D) Elimination of packet buffering
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |



