A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?

• A. HPE Private Cloud AI with NVIDIA
• B. An HPE AI Services - Transformation Workshop
• C. NVIDIA DGX Systems
• D. HPE Cray systems
• E. AI-optimized HPE ProLiant DL servers

What is the primary function of hidden layers in a deep learning model?

• A. To apply weights and transformations to the outputs of preceding layers, allowing the network to learn progressively more complex features.
• B. To directly receive the initial, raw input data for the model.
• C. To format the final output of the network into a human-readable prediction or classification.
• D. To store the complete training dataset for real-time retrieval during inference.

A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)

• A. The solution requires a Medium configuration.
• B. The key component is the HPE ProLiant DL325 server in the management plane.
• C. The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
• D. The solution requires a Large configuration.
• E. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.

A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?

• A. Large - Expanded, because its NVIDIA H100 NVL GPUs and eight worker nodes are required for the intensive fine-tuning workload.
• B. Medium - Expanded, because it supports a high number of concurrent users for RAG.
• C. Two separate solutions: a Small - Expanded for the RAG workload and a Large - Standard for the fine-tuning workload.
• D. A custom-built solution using HPE ProLiant DL325 servers and NVIDIA L4 GPUs.

An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?

• A. 3x HPE ProLiant DL325 Gen11 servers for management
• B. 4x HPE ProLiant DL380a Gen11 servers with a total of 16x NVIDIA H100 NVL GPUs
• C. 1x HPE ProLiant DL380a Gen11 server with 4x NVIDIA L40S GPUs
• D. 2x HPE ProLiant DL380a Gen11 servers with a total of 8x NVIDIA L40S GPUs