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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 20% | - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments |
| AI Infrastructure Components and Architecture | 30% | - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements |
| AI Infrastructure Operations and Troubleshooting | 20% | - Manage health, logs, and events - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues |
| AI Infrastructure Deployment and Data Management | 30% | - 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 - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
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NEW QUESTION # 15
Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?
Answer: C
Explanation:
A UCS Domain profile is deployed to fabric interconnects in Cisco Intersight Managed Mode and contains the domain-level configuration, including port policies, VLANs, VSANs, network connectivity, and other fabric interconnect settings.
NEW QUESTION # 16
A customer deploys a UCS 885a server in a data center with two power grids. Three of the
3000W power supplies in the server connect to each grid. Due to an external event, one of the grids will be shut down for maintenance. How does the server respond to this event?
Answer: A
Explanation:
With three 3000W power supplies lost from one power grid, the UCS C885A continues operating on the remaining grid but no longer has the full power budget available for maximum GPU operation. The server reduces performance by enforcing power limits, including capping GPU power, so workloads can continue running within the available power capacity.
NEW QUESTION # 17
An engineer must configure dynamic load balancing (DLB) on a Cisco Nexus 9000 Series Switch AI fabric using a CLI. Which configuration must be implemented for only the odd-numbered interfaces?
Answer: D
Explanation:
Dynamic load balancing is enabled on the selected Cisco Nexus 9000 interfaces by applying the interface-level dlb-interface command. For this deployment requirement, the command is applied only to the odd-numbered interfaces that participate in the DLB configuration.
NEW QUESTION # 18
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.
Answer:
Explanation:
NEW QUESTION # 19
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)
Answer: B,C
Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.
NEW QUESTION # 20
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