2026 Updated Practice 300-640 Online | 100% Free Valid Test Implementing Cisco Data Center AI Infrastructure Tutorial

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Cisco 300-640 Exam Syllabus Topics:

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

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Practice 300-640 Online | Professional Implementing Cisco Data Center AI Infrastructure 100% Free Valid Test Tutorial

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q81-Q86):

NEW QUESTION # 81
During validation testing, an engineer observes microbursts causing packet drops on GPU uplinks. Which monitoring capability best detects short-duration congestion events?

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

Answer: B

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 # 82
A company is building a network fabric for an AI training cluster that will train large language models. The cluster includes 256 GPUs distributed across 32 servers. Security requirements mandate isolation between different training projects and allowing shared access to a central storage system. Which approach provides the required security isolation and maintains optimal performance for GPU-to-GPU communication?

A. Implement port-based VLANs for each project with router ACLs controlling storage access at the distribution layer.
B. Configure QoS-based traffic steering with separate DSCP markings per project and policy-based routing to shared storage.
C. Deploy separate physical networks for each project with dedicated switches and cross-connects to shared storage.
D. Use VXLAN with separate VNIs per project and Group Policy Option to control granular access to shared storage.

Answer: D

Explanation:
VXLAN provides scalable network segmentation by assigning separate VNIs to different training projects, preserving isolation without building separate physical fabrics. Group Policy Option adds policy-based control within the VXLAN overlay, allowing controlled shared access to central storage while maintaining high-performance GPU-to-GPU communication across the AI training fabric.

NEW QUESTION # 83
What is a purpose of Cisco AI PODs?

A. to provide a turnkey, full-stack infrastructure for AI training, fine-tuning, and inferencing
B. to enable AI lifecycle management through cloud-native software with hardware support limited to compute servers
C. to offer a flexible AI infrastructure focused on inferencing workloads with optional training capabilities
D. to deliver modular AI hardware components that can be integrated with third-party software platforms

Answer: A

Explanation:
Cisco AI PODs are designed as integrated, validated full-stack infrastructure solutions that combine compute, networking, storage, and management capabilities to support AI training, fine- tuning, and inferencing workloads with faster deployment and operational consistency.

NEW QUESTION # 84
A Cisco AI infrastructure requires fast convergence after a spine switch failure. Which routing design principle best supports this objective?

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

Answer: C

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 # 85
An engineer must configure dynamic load balancing (DLB) on a Cisco Nexus 9000 Series Switch AI fabric using a CLI. Which configuration must be implemented for only the odd-numbered interfaces?

A. Create a QoS policy that enables DLB and apply that policy to the relevant interfaces.
B. Under the relevant interface-level configuration, add the keyword dlb-interface.
C. Exclude the desired interfaces from DLB with the interface level configuration of no dlb-interface.
D. Create a DLB hardware profile and include the list of desired interfaces.

Answer: B

Explanation:
Dynamic load balancing is enabled on the selected Cisco Nexus 9000 interfaces by applying the interface-level dlb-interface command. For this deployment requirement, the command is applied only to the odd-numbered interfaces that participate in the DLB configuration.

NEW QUESTION # 86
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