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Cisco 300-640 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure Security and Compliance | 10% | - Access Control and Authentication - Compliance Considerations - Security Best Practices |
| Cisco AI Infrastructure Architecture | 30% | - Cisco UCS AI Infrastructure Solutions - Hyperconverged Infrastructure for AI - Storage Architecture Considerations - Network Architecture for AI Workloads |
| AI Infrastructure Operations | 20% | - Performance Optimization - Monitoring and Management Tools - Capacity Planning - Troubleshooting Common Issues |
| AI Infrastructure Deployment | 25% | - Network Switch Configuration - Cabling and Connectivity - GPU Server Configuration - Hardware Installation and Configuration - Storage Configuration for AI |
| AI Infrastructure Fundamentals | 15% | - AI Infrastructure Requirements - GPU Architecture and Types - AI Workload Characteristics - AI Ecosystem Components |
Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:
1. 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?
A) basic server health reporting, which is limited to CPU and memory statistics, and requires specialized third-party tools for any GPU-specific performance monitoring or diagnostics
B) Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
C) comprehensive health and performance monitoring, which provides real-time telemetry data for UCS servers, including granular metrics for NVIDIA GPUs, which enables direct insights into AI workload execution
D) integration with Cisco Nexus Dashboard for advanced network flow analytics and anomaly detection across the data center fabric, which ensures high-speed data transfer between GPU nodes
2. An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?
A) Delta Analysis
B) Flow Telemetry
C) Connectivity Analysis
D) SNMP Traps
3. A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The current compute environment must co-exist with the newly purposed GPU-enabled workload. This environment will continue to grow, so the customer wants to scale out the resources as needed. Which Cisco product meets the requirements?
A) UCS C-series Standalone
B) Unified Edge
C) UCS X-series
D) UCS M-series
4. Which components are part of the HyperFabric AI solution?
A) Cisco UCS servers combined with high-speed networking switches and third-party storage
B) integrated compute and networking hardware optimized for AI workloads, with optional cloud management
C) combination of Cisco networking hardware and software-defined storage designed for AI scalability
D) Cisco UCS servers with NVIDIA GPUs, switches, optional VAST Data Storage and cloud- managed networking
5. What describes inference traffic patterns in AI deployments?
A) eliminates the need for data preparation
B) optimizes data storage solutions for data lakes
C) simplifies complex neural network architectures
D) provides real-time decision making
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |


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