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Cisco 300-640 Exam Syllabus Topics:

Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:

1. During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

A) Streaming telemetry with high-frequency sampling
B) Daily syslog review
C) Manual CLI inspection once per week
D) NetFlow with hourly exports

2. An engineer is deploying a Cisco AI POD environment to support large-scale AI workloads. The solution must provide a software-defined storage platform with a Disaggregated Shared- Everything architecture, allow compute nodes to run on Cisco UCS C225 M8 servers managed through Cisco Intersight, and deliver exabyte-scale, all-flash performance with integrated high- speed RDMA and GPU Direct Storage capabilities. Which solution meets the requirements?

A) Pure Storage FlashBlade
B) NetApp All-Flash FAS (AFF)
C) Local NVMe SSDs within each GPU server
D) VAST Data Universal Storage

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

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

4. Which components are part of the HyperFabric AI solution?

A) combination of Cisco networking hardware and software-defined storage designed for AI scalability
B) Cisco UCS servers combined with high-speed networking switches and third-party storage
C) Cisco UCS servers with NVIDIA GPUs, switches, optional VAST Data Storage and cloud- managed networking
D) integrated compute and networking hardware optimized for AI workloads, with optional cloud management

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) 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) 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) 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

Solutions: