Online Access Free HPE2-B08 Exam Questions

A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?

• A.The Medium configuration has more storage capacity than the Large configuration.
• B.The Large configuration does not support Retrieval-Augmented Generation (RAG).
• C.The Medium configuration is the only one that includes the HPE AI Essentials software.
• D.The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.

A data science team is struggling to manage their AI/ML projects. They use a variety of open-source tools for data preparation, training, and MLOps, but integrating them is complex and time-consuming.
They need a unified platform that provides self-service access to a curated and pre-integrated set of these tools.
Which HPE Private Cloud AI software component is specifically designed to solve this problem?

• A.HPE Intelligent Configurator
• B.NVIDIA NIM
• C.HPE AI Essentials
• D.HPE GreenLake for File Storage

A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?

• A.AI-optimized HPE ProLiant DL servers
• B.HPE Cray systems
• C.HPE GreenLake for File Storage
• D.HPE Private Cloud AI with NVIDIA, Large configuration

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

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.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.Apply stricter content moderation and safety guardrails to the model's output.