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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Manage health, logs, and events |
| Topic 2: 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 - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools - Implement data management and lifecycle for AI workloads |
| Topic 3: AI Infrastructure Components and Architecture | 30% | - 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 hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements |
| Topic 4: AI Infrastructure Fundamentals | 20% | - 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 - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio |
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NEW QUESTION # 74
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?
Answer: B
Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.
NEW QUESTION # 75
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.
Answer:
Explanation:
NEW QUESTION # 76
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: A,D
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 # 77
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: A
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 # 78
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
Answer: B
Explanation:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.
NEW QUESTION # 79
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