Cisco 300-640 : Implementing Cisco Data Center AI Infrastructure

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:

1. Which IP-based storage transport supports flow control between directly connected devices?

A) NVMe/TCP
B) NVMe/RoCE
C) Fibre Channel
D) iSCSI

2. An engineer made changes to the corporate DNS, which caused a Cisco UCS X-Series domain to lose connectivity to Intersight. To restore connectivity, the management interface DNS and NTP settings must be updated using the device console UI. Which configuration change must be made in Intersight to allow the updated DNS configuration to persist?

A) Create a network connectivity policy and apply it to the blade chassis.
B) Create a device connector policy and apply it to the blade chassis.
C) Edit the IMC access policy associated to the fabric interconnects.
D) Edit the domain profile deployed to the fabric interconnect.

3. A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?

A) Spirent
B) IXIA
C) iPerf
D) NCCL

4. When considering a rail-only network design, when would adding spine switches make sense?

A) when providing greater overall total bandwidth within a single scalable unit
B) when scaling out to support traffic between multiple scalable units
C) when needing a greater path redundancy within a single scalable unit
D) when increasing the overall available buffers that spines provide

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

Solutions: