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

During a discovery call, a customer from a telecommunications company explains their primary goal:
"We need to analyze network traffic patterns in real-time to detect anomalies that could indicate a security threat or a network outage." Which key use case for HPE Private Cloud AI does this represent?

• A. Code Generation
• B. Document Chat
• C. AI Recommender System
• D. AI Cybersecurity

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

A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?

• A. The data scientists would primarily use tools within HPE AI Essentials (like JupyterLab and MLFlow) for model development, while the operations team would use NVIDIA NIMs to deploy the finished models.
• B. The entire process for all stakeholders is managed through the server's iLO interface.
• C. Both the data scientists and the operations team would use the HPE Intelligent Configurator to manage the models.
• D. The data scientists would use NVIDIA NIMs to train the models, and the operations team would use HPE Data Fabric to serve them.

An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?

• A. Use a pre-trained computer vision model to extract text from the policy documents.
• B. Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
• C. Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
• D. Train a new LLM from scratch using only the company's policy documents to ensure data privacy.