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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - 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 storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements |
| Topic 3: AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events |
| Topic 4: AI Infrastructure Deployment and Data Management | 30% | - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight |
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NEW QUESTION # 54
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 # 55
What does workload distribution offer in an AI infrastructure with local and external resources?
Answer: A
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 # 56
A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?
Answer: D
Explanation:
Weighted scheduling allocates bandwidth according to configured priorities, ensuring critical AI traffic receives adequate forwarding resources during congestion. FIFO provides no prioritization, while MAC filtering and port mirroring are unrelated to congestion management or QoS scheduling.
NEW QUESTION # 57
A data center facility experienced a power outage, and the UCS X-Series servers did not restart automatically when power is restored. The configuration must be updated to prioritize these servers to boot as soon as possible to resume their work. Which set of power policy configurations must be used to meet these requirements?
Answer: D
Explanation:
The servers must be configured to power back on automatically after power is restored, so the restore behavior should be set to always power on. Setting the power priority to high ensures these UCS X-Series servers receive priority during power recovery, allowing them to resume workloads as quickly as possible.
NEW QUESTION # 58
Drag and Drop Question
A network engineer is designing congestion management for an AI cluster using RoCEv2 transport. Based on Cisco best practices, the engineer must understand how ECN and PFC work together to provide lossless Ethernet transport. Drag and drop the congestion management mechanisms from the left onto the corresponding descriptions on the right. Not all options are used.
Answer:
Explanation:
NEW QUESTION # 59
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