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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - 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 compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability |
| AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Manage health, logs, and events - Implement performance benchmarking and validation - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues |
| 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 - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools - Implement data management and lifecycle for AI workloads |
| AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts |
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NEW QUESTION # 20
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?
Answer: C
Explanation:
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.
NEW QUESTION # 21
A company implements multicast traffic optimization for distributed AI training nodes. Which protocol enables efficient multicast receiver discovery in IPv4 networks?
Answer: C
Explanation:
Internet Group Management Protocol (IGMP) allows hosts to join and leave multicast groups dynamically. This improves multicast efficiency for distributed AI workloads. ARP resolves MAC addresses, while LLDP and CDP are neighbor discovery protocols that do not manage multicast memberships.
NEW QUESTION # 22
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 # 23
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 # 24
A network administrator configures RDMA over Converged Ethernet version 2 (RoCEv2). Which transport protocol does RoCEv2 use at Layer 4?
Answer: A
Explanation:
RoCEv2 encapsulates RDMA traffic over UDP/IP, enabling routable RDMA communication across Layer 3 networks. TCP introduces retransmission and latency overhead unsuitable for high-performance GPU communication. SCTP and ICMP are not used for RoCEv2 transport.
NEW QUESTION # 25
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