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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Optimize performance, security, and reliability - Manage health, logs, and events - Troubleshoot network, compute, storage, and orchestration issues |
| AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
1. 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) BPDU Guard
B) Priority Flow Control
C) DHCP Snooping
D) Storm Control
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 compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?
A) UCS C885A M8 with NVIDIA H200 GPU
B) UCS C220 M8 with NVIDIA A16 GPU
C) UCS C845A M8 with AMD MI210 GPU
D) UCS C240 M8 with NVIDIA L40 GPU
3. A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?
A) Implement a dual-fabric design with two Cisco Fabric Interconnects, and use 100 Gb Ethernet for server-to-fabric connectivity.
Configuring port channels for uplink connections.
B) Connect UCS servers to a Cisco Nexus 3000 Series Switch using 100 Gb Ethernet.
Configure a separate management network using a dedicated Cisco Catalyst switch.
C) Deploy a Cisco MDS 9000 Series Switch for Fibre Channel connectivity to storage.
Use a Cisco Fabric Interconnect for management traffic only.
D) Use a single Cisco Fabric Interconnect in a standalone configuration.
Connect all UCS servers directly to it using 10 Gb Ethernet.
4. How does RAG enhance the capabilities of LLMs?
A) by providing access to proprietary data
B) by removing the data preparation and cleaning step
C) by retaining the LLM by altering its core parameters
D) by removing inherent biases from LLM outputs
5. An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?
A) OSPF
B) BGP EVPN
C) HSRP
D) STP
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
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