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| Section | Objectives |
|---|---|
| Topic 1: Data Center Networking for AI Workloads | - Cisco Data Center networking technologies
|
| Topic 2: AI Infrastructure Deployment and Operations | - Deployment models
|
| Topic 3: Data Center AI Infrastructure Fundamentals | - AI/ML workload requirements in data centers
|
| Topic 4: Security and Governance in AI Data Centers | - Infrastructure security
|
| Topic 5: Compute and Storage for AI Infrastructure | - Compute architecture
|
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NEW QUESTION # 51
A customer is deploying a new AI fabric with all new NVIDIA GPUs and must verify the performance between the nodes in the network. Which tool must be used to validate the customer benchmarks?
Answer: C
Explanation:
NCCL is the standard tool used to validate GPU-to-GPU communication performance in NVIDIA- based AI fabrics. It measures collective communication behavior between GPU nodes, making it appropriate for benchmarking the AI training network performance.
NEW QUESTION # 52
A network architect wants to provide high availability for AI compute nodes connected to dual Cisco Nexus switches. Which technology enables active-active uplink forwarding without STP blocking?
Answer: A
Explanation:
Virtual Port Channel (vPC) allows devices to use active-active uplinks across two Nexus switches while avoiding spanning-tree blocking. This increases bandwidth utilization and resiliency for AI clusters. GLBP and VRRP provide gateway redundancy, while PVST+ still relies on blocking redundant paths.
NEW QUESTION # 53
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?
Answer: C
Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.
NEW QUESTION # 54
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?
Answer: B
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 # 55
When considering a rail-only network design, when would adding spine switches make sense?
Answer: A
Explanation:
In a rail-only design, each scalable unit provides optimized east-west GPU communication within that unit. Spine switches are added when the design must scale beyond a single scalable unit and support traffic between multiple scalable units while preserving predictable performance for larger AI training environments.
NEW QUESTION # 56
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The goal of 300-640 preparation material is to help applicants prepare for the Implementing Cisco Data Center AI Infrastructure certification exam by providing them with the Actual 300-640 Exam Questions they need to pass the exam. This Implementing Cisco Data Center AI Infrastructure (300-640) study material is in the form of practice tests and 300-640 PDF that thoroughly covers the content of the test.
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