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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues |
| Topic 2: AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI use cases in data center environments - Describe AI and machine learning concepts |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - 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 - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure |
| Topic 4: AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate hybrid and multi-cloud AI integration |
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NEW QUESTION # 14
An administrator configures VXLAN EVPN in a Cisco AI data center fabric. The requirement is to provide Layer 2 adjacency across multiple leaf switches while maintaining scalable control-plane learning. Which protocol fulfills this function?
Answer: D
NEW QUESTION # 15
An end customer plans to deploy an AI fabric in a new data center. Their engineers are more familiar with CLI-based networks and require the capability to automate standardized configuration templates and monitor flows for the fabric. Which data center automation tool meets the requirements?
Answer: C
Explanation:
Nexus Dashboard is the Cisco data center automation and operations platform used with Nexus fabrics. It supports standardized fabric configuration through automation templates and provides visibility features such as flow monitoring, making it the appropriate tool for engineers who need CLI-oriented data center fabric operations with automation and telemetry.
NEW QUESTION # 16
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?
Answer: D
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 # 17
What does workload distribution offer in an AI infrastructure with local and external resources?
Answer: B
Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.
NEW QUESTION # 18
A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?
Answer: B
Explanation:
Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.
NEW QUESTION # 19
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