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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Components and Architecture | 30% | - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Deployment and Data Management | 30% | - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Deploy and manage AI infrastructure with orchestration tools - 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 |
| AI Infrastructure Operations and Troubleshooting | 20% | - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events - Optimize performance, security, and reliability |
| AI Infrastructure Fundamentals | 20% | - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe AI use cases in data center environments - Describe AI and machine learning concepts |
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NEW QUESTION # 17
When considering a rail-only network design, when would adding spine switches make sense?
Answer: D
Explanation:
In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.
NEW QUESTION # 18
What is the process involved in workload scheduling for AI environments?
Answer: C
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 # 19
Drag and Drop Question
Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.
Answer:
Explanation:
NEW QUESTION # 20
A Cisco AI environment requires synchronized packet processing timestamps across network devices for analytics accuracy. Which protocol should be deployed?
Answer: C
Explanation:
Precision Time Protocol (PTP) provides highly accurate clock synchronization required for latency-sensitive AI and analytics environments. NTP offers lower precision suitable for general IT systems. FTP and DNS are unrelated to time synchronization.
NEW QUESTION # 21
A customer deploys a UCS 885a server in a data center with two power grids. Three of the
3000W power supplies in the server connect to each grid. Due to an external event, one of the grids will be shut down for maintenance. How does the server respond to this event?
Answer: B
Explanation:
With three 3000W power supplies lost from one power grid, the UCS C885A continues operating on the remaining grid but no longer has the full power budget available for maximum GPU operation. The server reduces performance by enforcing power limits, including capping GPU power, so workloads can continue running within the available power capacity.
NEW QUESTION # 22
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