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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments |
| Topic 2: AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools - 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 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 |
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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,B
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
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: D
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 # 51
A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?
Answer: B
Explanation:
Priority Flow Control (PFC) allows selective pausing of traffic classes during congestion, which is critical for RoCEv2 environments carrying GPU-to-GPU communication. AI workloads are highly sensitive to packet loss and retransmissions. BPDU Guard and DHCP Snooping are security features, while Storm Control only limits broadcast or multicast traffic and does not create lossless behavior for RDMA traffic.
NEW QUESTION # 52
A financial company plans to deploy an AI training and inference infrastructure to support its customers. The team must use a solution that provides 96 GPUs, aligns with NVIDIA-compliant reference architectures, and enables cloud-based management of the entire on-premises network environment. Which Cisco solution meets the requirements?
Answer: B
Explanation:
Cisco HyperFabric AI Small AI Cluster matches the 96-GPU requirement because the validated HyperFabric reference architecture uses 12 Cisco UCS C885A M8 servers with NVIDIA HGX for a total of 96 GPUs. Cisco Nexus HyperFabric is also cloud-managed and compliant with NVIDIA Enterprise Reference Architecture, enabling centralized management of the on-premises AI network, GPU servers, and storage.
NEW QUESTION # 53
A Cisco AI environment requires synchronized packet processing timestamps across network devices for analytics accuracy. Which protocol should be deployed?
Answer: D
Explanation:
Precision Time Protocol (PTP) provides highly accurate clock synchronization required for latency-sensitive AI and analytics environments. NTP offers lower precision suitable for general IT systems. FTP and DNS are unrelated to time synchronization.
NEW QUESTION # 54
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