A customer is expanding their HPE Private Cloud AI "Medium" configuration to support a new generative AI inferencing workload. They are concerned about network congestion and latency, as the AI workload is known to generate large, sudden bursts of traffic between the compute nodes and the storage system.
The solution uses NVIDIA Spectrum SN4700M switches for the AI interconnect.
Which feature of these switches is specifically designed to handle bursty traffic and prevent packet loss in a lossless Ethernet fabric?

• A. Support for Fibre Channel over Ethernet (FCoE) encapsulation.
• B. Integrated Silicon Root of Trust to validate the switch firmware integrity.
• C. The ability to route traffic based on application-layer metadata.
• D. A fully shared buffer architecture that can dynamically absorb traffic bursts from any port.

An 'AI Pro' customer wants to deploy a solution to create 'digital twins' of their manufacturing equipment for predictive maintenance simulations. This workload requires significant GPU power for both the simulation and the underlying AI model.
Which HPE solution should be positioned as the primary platform for this advanced, data-center based workload?

• A. Individual HPE ProLiant DL320 Gen11 servers at the edge.
• B. HPE Private Cloud AI with NVIDIA.
• C. A standard data warehouse solution without GPU acceleration.
• D. The customer's existing VDI environment.

A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)

• A. A model architecture that includes pooling layers to reduce spatial dimensions
• B. A basic, fully connected Artificial Neural Network (ANN)
• C. A transformer-based model designed for Natural Language Processing (NLP)
• D. A model architecture that includes convolutional layers for feature extraction
• E. A Convolutional Neural Network (CNN)

A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)

• A. Increase the number of hidden layers in the model's architecture.
• B. Apply stricter content moderation and safety guardrails to the model's output.
• C. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
• D. Reduce the "temperature" setting during inference to make the model's output less random and more focused.
• E. Retrain the model from scratch using a much larger and more diverse public dataset.

An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?

• A. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
• B. It will require the architect to switch to the "application capacity" sizing method.
• C. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
• D. It will have no effect, as the number of users is the primary factor.