When an AI environment contains both local and external resources, workload distribution enables computational jobs to be placed or divided across multiple locations according to performance, capacity, latency, data residency, security, and cost requirements. This is a defining capability of hybrid AI infrastructure rather than an isolation mechanism.
Cisco defines hybrid AI infrastructure as combining on-premises and cloud resources within a common operating model. Workloads can use cloud capacity for burst training or experimentation while sensitive data or steady inference operations remain on premises. Cisco further identifies workload mobility, secure connectivity, and data synchronization as explicit requirements for hybrid AI deployment in DCAI objective 2.5. Cisco material on AI workload orchestration likewise describes workload execution across on-premises, cloud, and hybrid environments.
Option A unnecessarily isolates functions rather than distributing them. Option C contradicts hybrid automation by restricting operations locally. Option D describes security-policy enforcement, which is important but does not define workload distribution.
Accordingly, B is the technically correct response. It also corrects the answer indicated in the supplied source document, which lists C despite Cisco's current hybrid-infrastructure guidance.
Study Guide Reference: 1.0 AI Fundamentals and Applications — 1.4.b Hybrid; 2.5 Hybrid AI deployment and workload mobility.
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