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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure Fundamentals | 20% | - Describe AI use cases in data center environments - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI and machine learning concepts - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio |
| Topic 2: AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools |
| Topic 3: AI Infrastructure Operations and Troubleshooting | 20% | - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Optimize performance, security, and reliability |
| Topic 4: AI Infrastructure Components and Architecture | 30% | - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate hybrid and multi-cloud AI integration - Evaluate power, cooling, and efficiency requirements |
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NEW QUESTION # 27
Drag and Drop Question
An engineer must configure vNIC templates for a UCS X-Series deployment. Drag and drop the policy types from the left onto the corresponding configuration settings on the right. Not all options are used.
Answer:
Explanation:
NEW QUESTION # 28
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?
Answer: C
Explanation:
Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.
NEW QUESTION # 29
Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?
Answer: B
Explanation:
Rapid provisioning supports scalable AI workload execution by enabling computational platforms to be launched quickly and consistently as workload demand grows. This allows AI infrastructure to add or prepare compute resources efficiently for training, inference, or data-processing tasks.
NEW QUESTION # 30
Which function is provided through natural language processing?
Answer: B
Explanation:
Natural language processing enables systems to interpret, process, and transform human language from spoken or written inputs into structured data that applications can analyze, search, classify, or use for automation.
NEW QUESTION # 31
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)
Answer: B,D
Explanation:
Cisco Intersight provides GPU-focused telemetry for UCS servers, including utilization and performance metrics. These measurements allow the administrator to monitor how heavily the GPUs are being used and whether the AI workload is receiving the expected GPU performance.
NEW QUESTION # 32
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