3.2 AI Network Design Use Cases (such as Machine Learning, Large Language Models, and Pattern Recognition) 400-007 Practice Questions (Page 5)

Questions 21–25

1. 21foundation · easy

   What is the primary goal of flow scheduling optimization in an AI network?

   • ATo reduce the completion time of critical AI flows
   • BTo eliminate the need for buffering
   • CTo increase the number of flows in the network
   • DTo maximize the hop count for each flow
   Reveal Solution Discussion Select an answer first
2. 22foundation · easy

   Which data center topology characteristic is most beneficial for supporting distributed machine learning training?

   • AA topology that maximizes the number of hops between nodes to spread load
   • BA topology that provides multiple equal-cost paths between any two compute nodes
   • CA topology with a single core switch to simplify management
   • DA topology that uses only layer 2 switching to avoid routing overhead
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   In a data center network designed for distributed machine learning training, which traffic pattern is most important to optimize for?

   • ATraffic between storage and backup systems
   • BNorth-south traffic between clients and servers
   • CEast-west traffic between compute nodes
   • DTraffic between management and compute planes
   Reveal Solution Discussion Select an answer first
5. 24expert · hard

   A financial trading firm is deploying a machine learning training cluster for high-frequency trading models. The training jobs are time-sensitive and must complete as quickly as possible. The cluster uses a mix of GPU servers and high-performance storage. The network team must choose a congestion control mechanism for the fabric. The fabric uses a spine-and-leaf topology with shallow buffers. Which mechanism is most appropriate?

   • AEnable priority flow control (PFC) to prevent packet loss and use a lossless fabric for the training traffic
   • BUse a data center TCP variant like DCTCP with ECN to provide low-latency, high-throughput congestion control
   • CImplement a custom congestion control algorithm that uses packet loss as the primary signal for congestion
   • DUse standard TCP with tail-drop queuing to keep the design simple and rely on the application to handle retransmissions
   Reveal Solution Discussion Select an answer first
6. 25application · medium

   A large e-commerce company runs a real-time recommendation system that uses a distributed inference cluster. The system must respond to user requests in under 50 milliseconds. The network team needs to monitor the cluster to detect performance degradation before it impacts users. Which telemetry approach is most effective for this requirement?

   • ACollect and analyze sFlow samples from all switches every 30 seconds to detect traffic anomalies
   • BUse SNMP polling of interface counters every 5 minutes to monitor for errors and discards
   • CEnable streaming telemetry on the switches to export queue depth and latency metrics to a central analytics platform in real time
   • DDeploy a network tap on the core links and use a third-party tool to analyze packet captures offline
   Reveal Solution Discussion Select an answer first