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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Fundamentals | 20% | - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments - Describe AI and machine learning concepts |
| AI Infrastructure Components and Architecture | 30% | - Evaluate power, cooling, and efficiency requirements - 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 |
| AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools |
| AI Infrastructure Operations and Troubleshooting | 20% | - Optimize performance, security, and reliability - Implement performance benchmarking and validation - Manage health, logs, and events - Troubleshoot network, compute, storage, and orchestration issues - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts |
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NEW QUESTION # 79
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: C
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 # 80
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 # 81
Which network design approach should be used to build a high-performance, nonblocking AI/ML data center fabric that supports 64 fully equipped UCS C885A nodes and provides the lowest possible latency and lossless Ethernet?
Answer: A
Explanation:
A rail-optimized design is used for large AI/ML clusters because it aligns GPU-to-GPU communication paths across the fabric, minimizes latency, and supports high-throughput lossless Ethernet for distributed training workloads.
NEW QUESTION # 82
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 # 83
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: A
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 # 84
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