Online Access Free HPE2-B08 Practice Test

A customer wants to enhance their existing Large Language Model (LLM) to provide more accurate and contextually relevant answers based on a proprietary, rapidly changing knowledge base of legal documents. They are considering two approaches: fine-tuning and Retrieval-Augmented Generation (RAG).
Review the data flow diagram for the proposed RAG implementation:
```
User Query -> [Query Encoder] -> Vector DB Search -> [Retrieved Documents] --+
|
+-> [LLM Prompt] -> LLM -> Response
```
Based on the diagram and the scenario, which statement accurately identifies a primary advantage of the RAG approach for this customer?

• A.RAG permanently modifies the LLM's internal weights to specialize in legal terminology.
• B.RAG allows the LLM to access the most current legal documents at inference time without daily retraining.
• C.RAG requires retraining the LLM whenever a new legal document is added to the knowledge base.
• D.RAG reduces the need for a vector database by directly integrating documents into the model.

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

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

A customer states they need an AI solution for a "summarization" use case that will be accessed by approximately 75 concurrent users. The underlying data is updated frequently, so RAG will be required.
When using the HPE Intelligent Configurator, which three inputs are mandatory to get an initial sizing recommendation?

• A.Use case, Model choice, Network switch preference
• B.Use case, Number of users, RAG requirement
• C.Number of users, Preferred GPU model, Data center location
• D.RAG requirement, Power and cooling capacity, Desired software license term

Match the stakeholder in an AI project to their most likely primary concern.
Stakeholders:
1. Data Scientist
2. AI Decision Maker (CIO)
3. Data Engineer
Concerns:
a. "How can I easily build, manage, and automate data pipelines to feed our models?" b. "What is the total cost of ownership (TCO) and return on investment (ROI) for this AI initiative?" c. "Which hyperparameter tuning method will give my model the highest accuracy?"

• A.1-c, 2-a, 3-b
• B.1-c, 2-b, 3-a
• C.1-b, 2-a, 3-c
• D.1-a, 2-c, 3-b