300-640 Sample Exam - Free 300-640 Exam Dumps

Questions in desktop-based mock exams are identical to the real ones. Our practice exams give you options to change their durations and questions' numbers to polish your skills. You can easily assess your readiness with the assistance of results produced by the practice exam. This Implementing Cisco Data Center AI Infrastructure software records all your previous takes so you can identify your mistakes and overcome them before the final attempt. The Implementing Cisco Data Center AI Infrastructure (300-640) desktop practice exam software works only on Windows operating system.

Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Infrastructure Fundamentals	20%	- Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI and machine learning concepts - Describe AI use cases in data center environments - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Explain Cisco AI infrastructure solutions and portfolio
Topic 2: AI Infrastructure Operations and Troubleshooting	20%	- Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Troubleshoot network, compute, storage, and orchestration issues - Manage health, logs, and events - Implement performance benchmarking and validation
Topic 3: AI Infrastructure Components and Architecture	30%	- Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate power, cooling, and efficiency requirements - Evaluate hybrid and multi-cloud AI integration
Topic 4: AI Infrastructure Deployment and Data Management	30%	- Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q60-Q65):

NEW QUESTION # 60
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?

A. Implement a dual-fabric design with two Cisco Fabric Interconnects, and use 100 Gb Ethernet for server-to-fabric connectivity.
Configuring port channels for uplink connections.
B. Use a single Cisco Fabric Interconnect in a standalone configuration.
Connect all UCS servers directly to it using 10 Gb Ethernet.
C. Connect UCS servers to a Cisco Nexus 3000 Series Switch using 100 Gb Ethernet.
Configure a separate management network using a dedicated Cisco Catalyst switch.
D. Deploy a Cisco MDS 9000 Series Switch for Fibre Channel connectivity to storage.
Use a Cisco Fabric Interconnect for management traffic only.

Answer: A

Explanation:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.

NEW QUESTION # 61
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?

A. Equal-cost multipath routing
B. Single-path forwarding
C. Static route redistribution
D. Layer 2 loop dependency

Answer: A

Explanation:
ECMP provides multiple active forwarding paths and enables rapid failover when a path becomes unavailable. AI environments benefit from resilient high-bandwidth forwarding. Static redistribution slows operational flexibility, while single-path forwarding and Layer 2 dependency reduce scalability and convergence efficiency.

NEW QUESTION # 62
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. 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
C. Analyze feature automating the deployment and scaling of AI application containers and their underlying Kubernetes infrastructure, which reduces manual monitoring efforts
D. 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

Answer: B

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 # 63
A nationwide grocery chain has deployed Unified Edge to all its locations. Each store has 10 or more surveillance cameras that write approximately 1 Gb per hour. The surveillance retention policy requires maintaining 2 weeks of recording per camera. Which type of cost-effective centralized storage solution must be evaluated with Unified Edge devices to adhere to this policy?

A. Infiniband shared storage
B. IP-based shared storage
C. Fibre Channel shared storage
D. local storage

Answer: B

Explanation:
Unified Edge deployments that need centralized, cost-effective retention for large surveillance data volumes should evaluate IP-based shared storage. It provides scalable network-accessible capacity for multiple edge devices without requiring specialized Fibre Channel or InfiniBand infrastructure, making it appropriate for distributed video retention workloads.

NEW QUESTION # 64
What is a primary behavior that occurs during the model training stage of the AI lifecycle?

A. detecting model drift
B. establishing success criteria
C. embedding the model into systems with APIs
D. adjusting internal weights and biases

Answer: D

Explanation:
During model training, the model learns from training data by iteratively adjusting its internal weights and biases to reduce prediction error and improve performance against the defined objective function.

NEW QUESTION # 65
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