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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation - Manage health, logs, and events - Optimize performance, security, and reliability |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads |
| Topic 4: AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring |
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NEW QUESTION # 61
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: B
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 # 62
To support growing AI workload requirements, a regional medical practice is adding new X9516 X-Fabric module, UCS PCI Mezz cards, and UCS X580p PCIe nodes to their existing X-Series chassis. Which two administrative actions must be taken before the X-Fabric module is added to the chassis? (Choose two.)
Answer: B,D
Explanation:
Before adding the X9516 X-Fabric module, the affected X-Series blades must be powered down because the X-Fabric connectivity path requires hardware installation and cannot be safely introduced while the connected compute nodes are running. The UCS PCI Mezzanine cards must also be installed in the blades so they can physically connect through the X-Fabric module to the X580p PCIe nodes for GPU expansion.
NEW QUESTION # 63
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?
Answer: B
Explanation:
RoCEv2 encapsulates RDMA traffic over UDP/IP, enabling routable RDMA communication across Layer 3 networks. TCP introduces retransmission and latency overhead unsuitable for high-performance GPU communication. SCTP and ICMP are not used for RoCEv2 transport.
NEW QUESTION # 64
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?
Answer: A
Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.
NEW QUESTION # 65
What is a purpose of Cisco AI PODs?
Answer: D
Explanation:
Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.
NEW QUESTION # 66
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