300-640 dumps torrent: Implementing Cisco Data Center AI Infrastructure - 300-640 study materials

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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
AI Infrastructure Security and Compliance	10%	- Security Best Practices - Compliance Considerations - Access Control and Authentication
Cisco AI Infrastructure Architecture	30%	- Cisco UCS AI Infrastructure Solutions - Network Architecture for AI Workloads - Storage Architecture Considerations - Hyperconverged Infrastructure for AI
AI Infrastructure Fundamentals	15%	- AI Infrastructure Requirements - AI Workload Characteristics - GPU Architecture and Types - AI Ecosystem Components
AI Infrastructure Operations	20%	- Monitoring and Management Tools - Capacity Planning - Troubleshooting Common Issues - Performance Optimization
AI Infrastructure Deployment	25%	- Storage Configuration for AI - GPU Server Configuration - Hardware Installation and Configuration - Cabling and Connectivity - Network Switch Configuration

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q61-Q66):

NEW QUESTION # 61
Drag and Drop Question
An engineer must configure vNIC templates for a UCS X-Series deployment. Drag and drop the policy types from the left onto the corresponding configuration settings on the right. Not all options are used.

Answer:

Explanation:

NEW QUESTION # 62
Refer to the exhibit. Users report delayed response times and network latency when running certain AI workloads on a specific Cisco UCS Intersight Managed Mode Domain. Based on the fault message and reported symptoms, which troubleshooting action must be taken first to address the network latency issues?

A. Check the physical cabling and SFP+ transceivers on port 11 of switch B for potential hardware failures or incorrect configurations.
B. Review the AI workload configuration to optimize resource allocation (CPU, memory, GPU) for the affected applications.
C. Examine the UCS fabric interconnect configuration for port-channel 11 on Fabric Interconnect B.
D. Review the pause frame counters for signs of congestion on Fabric Interconnect B Port 11.

Answer: A

Explanation:
The fault shows a receive error statistics threshold crossing on switch B port 11. Because receive errors on a physical interface commonly indicate cabling, transceiver, or physical-layer issues, the first troubleshooting step is to inspect the physical connection and SFP+ components on the affected port before investigating workload or higher-layer configuration.

NEW QUESTION # 63
Which result is provided through image recognition using AI?

A. anonymization of audit trails for compliance objectives
B. scheduling of management tasks during maintenance periods
C. routing of multicast messages across network boundaries
D. detection and labeling of visual elements from digital sources

Answer: D

Explanation:
Image recognition uses AI to analyze digital images or video frames and identify, detect, classify, or label visual elements such as objects, people, patterns, or scenes.

NEW QUESTION # 64
A network engineer analyzes RoCEv2 traffic flows in an AI training cluster and notices that all RoCEv2 packets have the Don't Fragment bit set in the IP header. Why must RoCEv2 set the DF bit in all packets?

A. to enable Path MTU Discovery so that the optimal packet size can be negotiated
B. to ensure that packets always take the same path through ECMP routing for consistent latency
C. to prevent IP fragmentation that would break the InfiniBand sequence numbering and ordering guarantees
D. to indicate to intermediate switches that the packet contains RDMA traffic requiring special handling

Answer: C

Explanation:
RoCEv2 carries InfiniBand transport semantics over UDP/IP, and IP fragmentation would disrupt the expected packet sequencing and ordering behavior required by RDMA. Setting the Don't Fragment bit prevents intermediate devices from fragmenting RoCEv2 packets, preserving reliable RDMA transport behavior across the AI training fabric.

NEW QUESTION # 65
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

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

Answer: D

Explanation:
Microbursts occur within milliseconds and are often missed by traditional monitoring methods.
High-frequency streaming telemetry provides granular visibility into queue depth, drops, and latency conditions in real time. Hourly exports and infrequent manual inspection cannot reliably detect transient AI fabric congestion events.

NEW QUESTION # 66
......

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