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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions:

1. Which result is provided through image recognition using AI?

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

2. 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?

A) BGP EVPN route count per leaf switch
B) PFC pause frame transmission rate per interface
C) spanning tree topology change notifications
D) VXLAN tunnel packet encapsulation rate

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

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

4. A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?

A) using the maximum number of CPU cores to support the virtualization overhead
B) configuring the server with the minimum amount of RAM to reduce initial costs
C) selecting the appropriate Nexus Fabric and its configuration to minimize latency
D) implementing a redundant power supply configuration to ensure high availability

5. How does RAG enhance the capabilities of LLMs?

A) by removing the data preparation and cleaning step
B) by providing access to proprietary data
C) by retaining the LLM by altering its core parameters
D) by removing inherent biases from LLM outputs

Solutions: