An architect is designing an infrastructure solution for an AI workload that involves processing massive datasets for training. The goal is to minimize data transfer latency between the storage system and the GPUs in the compute nodes. The architect wants to enable the NVIDIA GPUs to fetch data directly from the NVMe storage array, bypassing the server's CPU and main memory.
Review the proposed architectural components:
```
- Compute Nodes: HPE ProLiant DL380a Gen11 with NVIDIA H100 GPUs
- Storage: HPE GreenLake for File Storage
- Interconnect: Ethernet with RoCE support
```
Which technology must be enabled and properly configured across these components to achieve this direct GPU-to-storage data path? (Choose 2.)

• A. RDMA over Converged Ethernet (RoCE)
• B. Multi-Instance GPU (MIG)
• C. Confidential Computing
• D. NVLink
• E. GPUDirect Storage (GDS)

A company is implementing a RAG-based chatbot using HPE Private Cloud AI. To ensure the chatbot provides safe and appropriate responses, the development team needs to implement guardrails to prevent it from discussing off-topic subjects and to block it from using harmful language.
Which specific toolkit within the NVIDIA NeMo framework is designed for this purpose?
```
NVIDIA NeMo Framework Components:
1. NeMo Curator
2. NeMo Customizer
3. NeMo Evaluator
4. NeMo Retriever
5. NeMo Guardrails
```

• A. NeMo Evaluator
• B. NeMo Guardrails
• C. NeMo Customizer
• D. NeMo Retriever
• E. NeMo Curator

An architect is designing a high-performance computing environment for a customer with two distinct, demanding workloads:
1. A large-scale, multi-node deep learning model that requires the fastest possible server-to-server communication.
2. A data-intensive analytics workload that requires the fastest possible data ingestion from an HPE GreenLake for File Storage array.
Which combination of technologies from the HPE Private Cloud AI solution should the architect select to optimize both workloads? (Select all that apply.)

• A. A flat TCP/IP network for simplicity, relying on the speed of the 200GbE adapters.
• B. Multi-Instance GPU (MIG) to partition GPUs for each workload.
• C. InfiniBand for the server-to-server fabric and Fibre Channel for the storage fabric.
• D. NVLink for intra-server GPU communication and RoCE for the server-to-server and server-to-storage fabric.
• E. GPUDirect Storage (GDS) to enable a direct data path between storage and GPUs for the analytics workload.

An enterprise is designing a solution for training a large, custom Convolutional Neural Network (CNN) for a new computer vision application. Their data science team has determined that the training process will need to be distributed across multiple GPUs to be completed in a reasonable timeframe. The training process involves intensive matrix multiplication operations.
The architect is specifying components from the HPE Private Cloud AI solution.
Which infrastructure components are critical for accelerating this specific distributed training workload?
(Select all that apply.)
```
Workload Analysis:
- AI Model: Large Convolutional Neural Network (CNN)
- Task: Distributed Training
- Key Operation: Intensive matrix multiplication
```

• A. HPE ProLiant DL325 servers for the worker nodes.
• B. NVIDIA GPUs featuring multiple Tensor Cores.
• C. HPE GreenLake for File Storage with standard NFS over TCP/IP.
• D. The HPE Data Fabric software component.
• E. An NVIDIA NVLink Bridge to connect the GPUs.

An architect is building a final configuration for an HPE Private Cloud AI solution using One Config Advanced (OCA). After selecting the "HPE Private Cloud AI - Medium Expanded" Smart Template, they review the generated Bill of Materials (BOM).
Which components are characteristic of the Medium Expanded configuration that the architect should expect to see in the OCA-generated BOM? (Select all that apply.)

• A. 16x NVIDIA H100 NVL GPUs
• B. 2x HPE Aruba Networking CX 6300M Switches
• C. 1x HPE ProLiant DL380a Gen11 Server
• D. 2x NVIDIA SN4700M Switches
• E. 16x NVIDIA L40S GPUs
• F. 4x HPE ProLiant DL380a Gen11 Servers