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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Deployment and Data Management | 30% | - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Deploy and manage AI infrastructure with orchestration tools - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics |
| AI Infrastructure Components and Architecture | 30% | - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate hybrid and multi-cloud AI integration - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS |
| AI Infrastructure Fundamentals | 20% | - Describe AI and machine learning concepts - Explain Cisco AI infrastructure solutions and portfolio - Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI use cases in data center environments |
| 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 - Optimize performance, security, and reliability - Manage health, logs, and events |
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NEW QUESTION # 34
A solutions architect must implement a monitoring strategy for a new AI/ML data center infrastructure built on Cisco UCS X-Series servers with NVIDIA H100 GPUs, managed by Cisco Intersight. The main objectives are to gain deep visibility into GPU performance, identify resource bottlenecks for AI training workloads, and ensure the overall health and efficiency of the compute environment. Which Cisco Intersight capability is most critical for effectively monitoring this AI- centric infrastructure?
Answer: D
Explanation:
Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.
NEW QUESTION # 35
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?
Answer: C
Explanation:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.
NEW QUESTION # 36
A data center operator configures telemetry streaming on Cisco Nexus switches. What is the primary operational advantage over traditional SNMP polling?
Answer: C
Explanation:
Streaming telemetry continuously exports operational data in near real time, improving visibility and reducing polling overhead compared to SNMP. AI infrastructures require rapid monitoring of congestion and latency conditions. Telemetry does not remove routing tables or require hardware replacement.
NEW QUESTION # 37
A Cisco AI cluster requires segmentation between multiple research teams sharing the same physical infrastructure. Which solution provides scalable tenant isolation?
Answer: D
Explanation:
VXLAN overlays provide scalable multi-tenant segmentation using large VNI spaces beyond traditional VLAN limitations. AI infrastructures often support many isolated environments simultaneously. VLAN-only designs have scalability constraints, while WAN topologies and NAT do not provide modern data center tenant segmentation.
NEW QUESTION # 38
How does RAG enhance the capabilities of LLMs?
Answer: D
Explanation:
RAG enhances LLMs by retrieving relevant external or proprietary information at generation time and using that context to produce more accurate, current, and domain-specific responses without changing the model's core parameters.
NEW QUESTION # 39
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