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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe AI and machine learning concepts - 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 |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration |
| Topic 3: AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - 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% | - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation - Optimize performance, security, and reliability - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
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NEW QUESTION # 14
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 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.
NEW QUESTION # 15
A network engineer logs into Nexus Dashboard and sees an anomaly showing that a BGP peer connection has gone down. Next to the anomaly, a yellow bubble displays "Correlated." What does Nexus Dashboard indicate by "Correlated" in this case?
Answer: C
Explanation:
In Nexus Dashboard, a correlated anomaly means the displayed issue is associated with a higher-level root anomaly. The BGP peer-down event is being treated as a resulting or related symptom rather than the primary root cause, helping operators focus troubleshooting on the upstream issue first.
NEW QUESTION # 16
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?
Answer: A
Explanation:
Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.
NEW QUESTION # 17
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?
Answer: C
Explanation:
Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.
NEW QUESTION # 18
An engineer must optimize the performance of an AI inference workload running on a Cisco UCS C-Series rack server. The workload experiences intermittent latency spikes, and Intersight system logs show frequent thermal event entries. Which troubleshooting action must be taken first to address the performance issues on the Cisco UCS server?
Answer: A
Explanation:
Frequent thermal events indicate that the server may be throttling CPU or GPU performance to protect hardware from overheating. The first action is to verify the physical cooling conditions, including ambient temperature, airflow, and fan health, because resolving thermal constraints directly addresses the likely cause of intermittent latency spikes.
NEW QUESTION # 19
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