A is correct. The HPE Private Cloud AI Developer System is positioned as a smaller, cost-conscious configuration for development and proof-of-concept work. Integrating file and object storage into the AI-optimized node reduces the need to purchase, cable, power, and administer a separate external storage tier for the entry configuration. That consolidation lowers infrastructure cost while preserving the data-access patterns required by notebooks, model assets, and AI development workflows.
The remaining choices contradict the design intent of HPE Private Cloud AI. An AI-optimized node is not made cost-effective by eliminating GPU acceleration and using CPU-only compute; doing so would undermine the target AI workload. HPE also does not reduce value by stripping out the integrated software toolchain. The platform’s advantage is precisely the curated, validated software environment. NVIDIA AI Enterprise support is likewise a core differentiator of HPE Private Cloud AI with NVIDIA rather than an optional capability removed to save money.
For exam purposes, distinguish architectural consolidation from functional reduction. HPE minimizes entry-system cost by integrating necessary infrastructure components, not by removing the accelerated-compute or enterprise-AI capabilities that define the solution.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 1 “HPE ProLiant Gen12 for AI” and HPE solutions for AI; HPE Private Cloud AI engineered-system and Developer System materials.
