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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Optimize performance, security, and reliability - Manage health, logs, and events |
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate hybrid and multi-cloud AI integration - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - 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 |
| AI Infrastructure Fundamentals | 20% | - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge |
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質問 # 20
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.)
正解：C、D
解説：
Cisco Intersight provides GPU-focused telemetry for UCS servers, including utilization and performance metrics. These measurements allow the administrator to monitor how heavily the GPUs are being used and whether the AI workload is receiving the expected GPU performance.
質問 # 21
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?
正解：B
解説：
Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.
質問 # 22
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
正解：C
解説：
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.
質問 # 23
An engineer deploys BGP EVPN in a Cisco VXLAN fabric. Which route type advertises MAC and IP binding information for endpoints?
正解：C
解説：
EVPN Type 2 routes advertise MAC and optional IP bindings for endpoints, enabling efficient host reachability in VXLAN overlays. Type 5 routes advertise IP prefixes, while Types 1 and 4 handle Ethernet segment and multihoming information. AI fabrics rely heavily on accurate endpoint advertisement for scalability.
質問 # 24
A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?
正解：D
解説：
VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.
質問 # 25
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