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Cisco 300-640 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Center AI Infrastructure Fundamentals	- AI/ML workload requirements in data centers 1. Data pipeline and workload characteristics 2. Compute, GPU, and acceleration concepts
Topic 2: Security and Governance in AI Data Centers	- Infrastructure security 1. Secure access to AI workloads 2. Network segmentation and policy enforcement
Topic 3: AI Infrastructure Deployment and Operations	- Operations and lifecycle management 1. Performance tuning and optimization 2. Monitoring and observability - Deployment models 1. Hybrid AI infrastructure integration 2. On-premises AI clusters
Topic 4: Data Center Networking for AI Workloads	- Cisco Data Center networking technologies 1. Cisco Nexus platform concepts 2. Cisco ACI integration concepts - High-performance fabric design 1. Low-latency switching and routing considerations 2. Leaf-spine architecture in AI environments
Topic 5: Compute and Storage for AI Infrastructure	- Compute architecture 1. GPU-based compute nodes 2. HPC-style scaling considerations - Storage systems 1. Distributed storage concepts 2. High-throughput storage for AI training data

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q27-Q32):

NEW QUESTION # 27
What does workload distribution offer in an AI infrastructure with local and external resources?

A. isolation of analytic functions within remote processing clusters
B. enforcement of fixed security policies on distribution endpoints
C. restriction of automation to local storage and performance measures
D. distribution of computational tasks across multiple locations

Answer: C

Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.

NEW QUESTION # 28
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. 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
B. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
C. 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
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

Answer: D

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 # 29
A Cisco Nexus switch experiences head-of-line blocking in an RDMA environment. Which mechanism is most effective in reducing this condition?

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

Answer: A

Explanation:
Virtual output queuing (VOQ) prevents packets destined for congested outputs from blocking unrelated traffic flows. This is valuable in AI infrastructures where high-bandwidth GPU traffic can overwhelm buffers. WRED manages congestion probabilistically but does not eliminate head-of- line blocking. PortFast and NAT overload are unrelated features.

NEW QUESTION # 30
Which components are part of the HyperFabric AI solution?

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

Answer: B

Explanation:
HyperFabric AI combines Cisco UCS servers with NVIDIA GPUs, Cisco Nexus 6000 Series switches, optional VAST Data storage, and cloud-managed networking to provide an integrated AI infrastructure solution for training and inference workloads.

NEW QUESTION # 31
Drag and Drop Question
An organization is deploying an AI/ML infrastructure with multiple network fabrics, each serving a specific purpose. The network architect must match the network fabric type with its primary function. Drag and drop the network fabric types from the left onto the corresponding descriptions on the right.

Answer:

Explanation:

NEW QUESTION # 32
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