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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - 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 - Evaluate power, cooling, and efficiency requirements - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability |
| AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio |
| AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads |
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NEW QUESTION # 77
A company is building a network fabric for an AI training cluster that will train large language models. The cluster includes 256 GPUs distributed across 32 servers. Security requirements mandate isolation between different training projects and allowing shared access to a central storage system. Which approach provides the required security isolation and maintains optimal performance for GPU-to-GPU communication?
Answer: B
Explanation:
VXLAN provides scalable network segmentation by assigning separate VNIs to different training projects, preserving isolation without building separate physical fabrics. Group Policy Option adds policy-based control within the VXLAN overlay, allowing controlled shared access to central storage while maintaining high-performance GPU-to-GPU communication across the AI training fabric.
NEW QUESTION # 78
What describes inference traffic patterns in AI deployments?
Answer: D
Explanation:
Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.
NEW QUESTION # 79
An Intersight administrator plans to deploy new server solutions to several small branch offices across the country. Each location needs at least one chassis. The servers must have redundant CPUs, memory, and 200 Gbps of unified fabric connectivity per compute node. Which hybrid AI compute solution meets the requirements?
Answer: B
Explanation:
Cisco UCS X-Direct is designed for smaller distributed or branch deployments that still need chassis-based UCS X-Series compute capabilities. It supports redundant compute resources and high-speed unified fabric connectivity, including 200 Gbps per compute node, making it the best fit for hybrid AI workloads across multiple branch office locations.
NEW QUESTION # 80
What is a function of NVLink technology within a high-performance AI server or cluster?
Answer: B
Explanation:
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.
NEW QUESTION # 81
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?
Answer: A
Explanation:
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.
NEW QUESTION # 82
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