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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Components and Architecture | 30% | - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| Topic 2: AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - 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 |
| Topic 4: AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Manage health, logs, and events - Troubleshoot network, compute, storage, and orchestration issues |
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NEW QUESTION # 33
When considering a rail-only network design, when would adding spine switches make sense?
Answer: D
Explanation:
In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.
NEW QUESTION # 34
What is the process involved in workload scheduling for AI environments?
Answer: D
Explanation:
Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.
NEW QUESTION # 35
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: C
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 # 36
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?
Answer: A
Explanation:
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.
NEW QUESTION # 37
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?
Answer: C
Explanation:
Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.
NEW QUESTION # 38
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