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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events - Optimize performance, security, and reliability - Troubleshoot network, compute, storage, and orchestration issues - Implement performance benchmarking and validation |
| AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts |
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads |
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NEW QUESTION # 43
An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?
Answer: B
Explanation:
Traffic Analytics and Flow Telemetry use overlapping telemetry resources in Cisco Nexus Dashboard. When Traffic Analytics is enabled to monitor congestion, latency, and drops, Flow Telemetry is automatically disabled to avoid resource and data collection conflicts.
NEW QUESTION # 44
What describes inference traffic patterns in AI deployments?
Answer: B
Explanation:
Inference traffic is associated with using a trained model to generate predictions or responses from new input data. In AI deployments, this commonly supports real-time or near-real-time decision making, such as classification, recommendations, detection, or automated responses.
NEW QUESTION # 45
What does workload distribution offer in an AI infrastructure with local and external resources?
Answer: B
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 # 46
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: B
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 # 47
A medium-sized breadmaking operation must deploy a fleet of GPU servers that meet these requirements for their flagship application:
- policy managed through cloud-based interface
- low latency between specific pairs of GPUs
- at least 2 GPUs per server
- all GPUs should be same model
- Intel CPU
- well suited for Inferencing
- customer is power conscious
Which server meets the requirements?
Answer: D
Explanation:
The UCS X210c M8 with the X580 PCIe node fits the requirements because it supports cloud- based policy management through Cisco Intersight, uses Intel Xeon processors, supports multiple same-model GPUs, and is well suited for GPU-accelerated inference workloads while allowing modular scale-out as demand grows. Cisco describes the X580p PCIe node as supporting up to four GPUs with X210c/X215c compute nodes for workloads including inference.
Reference:
https://www.cisco.com/c/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/mlperf-inference-ucs-x580p-wp.html
https://www.cisco.com/c/dam/en/us/products/collateral/servers-unified-computing/ucs-x-series- modular-system/x210cm8-specsheet.pdf
NEW QUESTION # 48
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