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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Deployment and Data Management | 30% | - Implement data management and lifecycle for AI workloads - 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 - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
| Topic 2: AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - 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 |
| Topic 3: AI Infrastructure Fundamentals | 20% | - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI and machine learning concepts - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments |
| Topic 4: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Optimize performance, security, and reliability |
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NEW QUESTION # 26
What is the process involved in workload scheduling for AI environments?
Answer: D
Explanation:
Workload scheduling in AI environments assigns jobs or tasks to available compute resources such as CPUs, GPUs, or accelerator nodes to improve utilization, reduce wait time, and optimize overall workload performance.
NEW QUESTION # 27
Server administrators receive reports of power utilization being near capacity across multiple UCS X-Series servers in the data center after a hardware upgrade. The chassis operates with six power supply units in redundant power mode. How can the administrator use additional power without impacting redundancy?
Answer: A
Explanation:
Extended power capacity allows a UCS X-Series chassis with six power supplies in redundant mode to make additional power available while still preserving redundancy. This is used when power demand increases after hardware upgrades and the administrator needs more usable power without changing the redundant power configuration.
NEW QUESTION # 28
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: C
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 # 29
Which IP-based storage transport supports flow control between directly connected devices?
Answer: A
Explanation:
NVMe/RoCE uses RDMA over Converged Ethernet and relies on lossless Ethernet behavior, including priority-based flow control between directly connected devices. This makes it suitable for high-performance AI storage traffic that needs low latency and minimal packet loss.
NEW QUESTION # 30
A medical company has existing third-party AI servers and NVMe storage, but they want to purchase Cisco network switches with a cloud managed feature for simplicity. Which solution best meets the requirements?
Answer: B
Explanation:
Bring Your Own AI HyperFabric is the best fit when the customer already has third-party AI servers and NVMe storage but wants Cisco 6000 Series switching with simplified cloud-managed fabric operations. The Premier license aligns with the AI-focused HyperFabric use case, while Cisco describes Nexus HyperFabric as a cloud-managed AI infrastructure solution using Cisco
6000 Series switches for AI workloads.
NEW QUESTION # 31
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