Question: In a 5G system supporting Artificial Intelligence (AI)/Machine Learning (ML) operations, what is the primary intention behind splitting AI/ML operations between AI/ML endpoints (e.g., a User Equipment (UE) and a network endpoint)?
Option 1: To enhance privacy by keeping all computations on the UE.
Option 2: To reduce latency by offloading all computations to the network endpoint.
Option 3: To optimize resource utilization by offloading computation-intensive parts to the network endpoint while keeping privacy-sensitive and delay-sensitive parts on the UE.
Option 4: To increase storage capacity by distributing models across both endpoints.
Explanation: This option correctly identifies the primary intention behind AI/ML operation splitting in a 5G system. By offloading computation-intensive, energy-intensive parts to network endpoints, the system conserves UE resources (e.g., battery life) while keeping sensitive operations on the device to maintain privacy and minimize latency for those specific tasks. This approach balances performance, efficiency, and user data protection.