300-640 Study Tool - 300-640 Test Torrent & Implementing Cisco Data Center AI Infrastructure Guide Torrent

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Cisco 300-640 Exam Syllabus Topics:

Section	Objectives
Data Center Networking for AI Workloads	- High-performance fabric design 1. Low-latency switching and routing considerations 2. Leaf-spine architecture in AI environments - Cisco Data Center networking technologies 1. Cisco Nexus platform concepts 2. Cisco ACI integration concepts
Data Center AI Infrastructure Fundamentals	- AI/ML workload requirements in data centers 1. Data pipeline and workload characteristics 2. Compute, GPU, and acceleration concepts
Security and Governance in AI Data Centers	- Infrastructure security 1. Secure access to AI workloads 2. Network segmentation and policy enforcement
AI Infrastructure Deployment and Operations	- Deployment models 1. On-premises AI clusters 2. Hybrid AI infrastructure integration - Operations and lifecycle management 1. Monitoring and observability 2. Performance tuning and optimization
Compute and Storage for AI Infrastructure	- Storage systems 1. High-throughput storage for AI training data 2. Distributed storage concepts - Compute architecture 1. GPU-based compute nodes 2. HPC-style scaling considerations

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q36-Q41):

NEW QUESTION # 36
A network engineer logs into Nexus Dashboard and sees an anomaly showing that a BGP peer connection has gone down. Next to the anomaly, a yellow bubble displays "Correlated." What does Nexus Dashboard indicate by "Correlated" in this case?

A. It indicates that this anomaly is a result of a higher-level root anomaly.
B. It indicates that the Nexus Dashboard has seen this anomaly across more than one device in the fabric.
C. It indicates that the Nexus Dashboard has correlated this anomaly to activity by specific user account.
D. It indicates that the Nexus Dashboard has correlated the scope of the impact.

Answer: A

Explanation:
In Nexus Dashboard, a correlated anomaly means the displayed issue is associated with a higher-level root anomaly. The BGP peer-down event is being treated as a resulting or related symptom rather than the primary root cause, helping operators focus troubleshooting on the upstream issue first.

NEW QUESTION # 37
A bank is planning to deploy an AI inference solution at its branch locations. The solution must support workloads that require a balance of compute, memory, and potential GPU acceleration, and it must be suitable for installation by nontechnical onsite resources. The requirements are:
- high availability of the chassis management plane.
- support for up to 768 GB of memory for in-memory model storage and
processing
- remote server launch requiring no on-site IT staff
- use of Cisco Intersight for SaaS-based infrastructure lifecycle
management
Which solution meets the requirements?

A. Cisco Unified Edge
B. Cisco UCS C240 Servers
C. Cisco UCS C845A M8 Server
D. Cisco UCS X-Series Direct

Answer: A

Explanation:
Cisco Unified Edge is designed for distributed branch and edge AI deployments that need simplified remote operations through Cisco Intersight. It supports branch-friendly installation, SaaS-based lifecycle management, high-availability chassis management, up to 768 GB of memory, and balanced compute resources for inference workloads that may require GPU acceleration.

NEW QUESTION # 38
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. 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. 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

Answer: A

Explanation:
Cisco Intersight provides health and performance monitoring for UCS servers, including real-time telemetry and GPU-specific metrics for NVIDIA GPUs. This visibility is essential for identifying bottlenecks, tracking AI training workload behavior, and maintaining the health and efficiency of GPU-accelerated UCS X-Series infrastructure.

NEW QUESTION # 39
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. UCS X-series
C. Unified Edge
D. UCS M-series

Answer: B

Explanation:
Cisco UCS X-Series is designed for scalable, modular compute environments that support GPU- enabled workloads, high memory capacity, virtualization, and coexistence with existing infrastructure. Its modular architecture allows the enterprise to expand resources as AI workload demands grow.

NEW QUESTION # 40
How does RAG enhance the capabilities of LLMs?

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

Answer: B

Explanation:
RAG enhances LLMs by retrieving relevant external or proprietary information at generation time and using that context to produce more accurate, current, and domain-specific responses without changing the model's core parameters.

NEW QUESTION # 41
......

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