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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 20% | - 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 - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Explain Cisco AI infrastructure solutions and portfolio |
| 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 - Integrate AI frameworks and applications with Cisco infrastructure - Deploy and manage AI infrastructure with orchestration tools |
| AI Infrastructure Operations and Troubleshooting | 20% | - Troubleshoot network, compute, storage, and orchestration issues - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Manage health, logs, and events - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
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NEW QUESTION # 27
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?
Answer: D
Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.
NEW QUESTION # 28
A national law firm is deploying a RAG solution for their legal research team to more efficiently use their 50 TB of confidential data and documents when preparing new contracts and briefs.
They decided on a UCS X-Series chassis equipped with four GPUs and six X210c servers. Which storage solution fits this deployment?
Answer: C
Explanation:
A RAG deployment with 50 TB of confidential documents requires scalable shared storage that can be accessed by multiple UCS X210c servers. An IP-based storage array provides centralized capacity, shared access, and operational simplicity for storing and retrieving large datasets used by the RAG pipeline.
NEW QUESTION # 29
A large-scale retail store manages many compute resources spread across branch offices, which enables AI workloads to be processed on-premises. Each workload and site location requires specific OS installation and configuration. To simplify management, configuration, and deployment of the workloads, a Cisco Intersight feature must be used that enables workload deployment at scale. Which feature must be used to accomplish this functionality?
Answer: A
Explanation:
Fleet management in Cisco Intersight is used to manage and deploy workloads consistently across many distributed compute resources. It supports scalable operations such as OS installation, configuration, and policy-driven deployment across multiple servers and locations, making it the correct feature for large distributed AI workload environments.
NEW QUESTION # 30
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?
Answer: B
Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.
NEW QUESTION # 31
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 # 32
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