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Cisco 300-640 Exam Syllabus Topics:

Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:

1. Which type of Cisco Intersight profile, when deployed to fabric interconnects, includes the configuration settings for ports, VLANs, and VSANs?

A) Unified Edge profiles
B) UCS Chassis profiles
C) UCS Server profiles
D) UCS Domain profiles

2. Which aspect of rapid provisioning supports a scalable workload execution in an AI infrastructure?

A) distinct management for isolated components
B) delayed rollout of new network resources
C) efficient launch of computational platforms
D) limited connectivity within isolated locations

3. A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

A) NAT overload
B) Virtual output queuing
C) Weighted Random Early Detection
D) PortFast

4. A Cisco Nexus switch is configured with QoS policies for AI traffic. Which scheduling mechanism guarantees bandwidth for critical traffic classes during congestion?

A) MAC filtering
B) FIFO
C) Port mirroring
D) Weighted scheduling

5. 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) Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
B) 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
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) 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

Solutions: