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Cisco 300-640 Exam Syllabus Topics:

Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:

1. Drag and Drop Question

Refer to the exhibit. When a new AI backend network that supports Cisco UCS-based GPU servers with NVIDIA Connect-X network adapters is benchmarked, the traffic distribution observed has unacceptable variability across the interfaces. Drag and drop the code snippets from the bottom onto the boxes in the code to configure the switch to resolve the issue.

2. An engineer deploys BGP EVPN in a Cisco VXLAN fabric. Which route type advertises MAC and IP binding information for endpoints?

A) Type 1
B) Type 5
C) Type 2
D) Type 4

3. 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?

A) The server initiates a graceful shut down of the operating system.
B) The server immediately shuts down and stops all operations.
C) The server continues to operate at full performance using redundant power supplies, but the PSU status LED glows solid amber.
D) The server operates with reduced performance and caps power to GPUs at 60%.

4. How does RAG enhance the capabilities of LLMs?

A) by removing inherent biases from LLM outputs
B) by retaining the LLM by altering its core parameters
C) by providing access to proprietary data
D) by removing the data preparation and cleaning step

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: