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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Components and Architecture | 30% | - 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 - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| Topic 2: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Manage health, logs, and events - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues |
| Topic 3: 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 and machine learning concepts - Describe AI use cases in data center environments |
| Topic 4: AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Deploy and manage AI infrastructure with orchestration tools - Integrate AI frameworks and applications with Cisco infrastructure |
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NEW QUESTION # 48
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?
Answer: A
Explanation:
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.
NEW QUESTION # 49
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.)
Answer: A,E
Explanation:
Cisco Intersight performance monitoring uses resource utilization metrics to identify bottlenecks in AI workloads. Active CPU utilization shows whether host processing is limiting workload execution, while GPU memory utilization shows how effectively GPU memory is being consumed during model processing and training or inference tasks.
NEW QUESTION # 50
A network team is configuring quality of service (QoS) for RoCEv2 traffic on a VXLAN fabric. The team must ensure that congestion notification packets (CNP) generated by storage targets are never dropped in the network, as these packets are critical for congestion control feedback. How must CNP packets be handled in the QoS configuration to ensure reliable delivery?
Answer: A
Explanation:
CNP packets provide congestion feedback for RoCEv2 traffic and must be delivered reliably so senders can reduce their transmission rate when congestion is detected. Classifying CNP packets into a high-priority queue based on their DSCP marking ensures that these control packets receive preferential treatment and are not dropped during congestion.
NEW QUESTION # 51
A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?
Answer: A
Explanation:
Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.
NEW QUESTION # 52
Drag and Drop Question
An organization is deploying an AI/ML infrastructure with multiple network fabrics, each serving a specific purpose. The network architect must match the network fabric type with its primary function. Drag and drop the network fabric types from the left onto the corresponding descriptions on the right.
Answer:
Explanation:
NEW QUESTION # 53
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