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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation |
| Topic 2: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts |
| Topic 3: 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 |
| Topic 4: AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Integrate AI frameworks and applications with Cisco infrastructure |
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NEW QUESTION # 10
An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?
Answer: B
Explanation:
VAST Data Universal Storage provides a software-defined, disaggregated shared-everything storage architecture designed for AI-scale workloads. It supports Cisco UCS-based deployments managed through Intersight and delivers high-performance all-flash storage with RDMA and GPU Direct Storage capabilities for large-scale AI training, fine-tuning, and inference environments.
NEW QUESTION # 11
An organization deploys a new AI training fabric that uses RoCEv2 for GPU communication. The network architect designs the QoS configuration to ensure reliable RDMA transport and must meet these requirements:
- Support 256 GPU servers with RDMA connectivity.
- Prevent any packet loss that causes RDMA connection failures.
- Maintain consistent low-latency communication with a target of less
than 10 microseconds.
- Use industry-standard protocols and configurations.
Which configuration ensures that RoCEv2 operates as a lossless transport?
Answer: B
Explanation:
RoCEv2 requires lossless Ethernet behavior to prevent packet drops that can disrupt RDMA communication. Enabling Priority Flow Control on the traffic class carrying RoCEv2 packets allows Ethernet pause behavior for that priority, helping maintain reliable, low-latency RDMA transport across the AI training fabric.
NEW QUESTION # 12
A Cisco UCS C885A M8 server contains a GPU sled configured with six power supply units (PSUs) in an N+2 redundancy configuration. The workload on the GPU sled is currently drawing a load of 4500W. Using Power Save Mode, how many PSUs can be placed into standby mode to improve power efficiency without compromising redundancy?
Answer: A
Explanation:
With six PSUs in an N+2 configuration and a 4500W load, at least two PSUs must remain available to carry the active workload, and two additional PSUs must remain available to preserve redundancy. Therefore, four PSUs must stay active, allowing two PSUs to be placed into standby mode for power efficiency without compromising the N+2 redundancy requirement.
NEW QUESTION # 13
Drag and Drop Question
An organization is deploying an AI/ML infrastructure with multiple network fabrics, each serving a specific purpose. The network architect must match the network fabric type with its primary function. Drag and drop the network fabric types from the left onto the corresponding descriptions on the right.
Answer:
Explanation:
NEW QUESTION # 14
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?
Answer: B
Explanation:
Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.
NEW QUESTION # 15
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