HPE Private Cloud AI Solutions - HPE2-B08

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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

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

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

3. An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```

A) A process for daily fine-tuning of the base LLM on all new research papers.
B) A Retrieval-Augmented Generation (RAG) framework to query the vector database.
C) A vector database to store embeddings of the medical research papers.
D) A pre-trained, general-purpose Large Language Model (LLM).
E) A data ingestion and embedding pipeline to process and add new research to the vector database.
F) An edge-optimized server for model deployment in individual hospital rooms.

4. A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?

A) A model development and experimentation workload.
B) A large-scale model training workload.
C) A classic AI inferencing workload.
D) A RAG (Retrieval-Augmented Generation) inferencing workload.

5. A customer has used the HPE Intelligent Configurator and determined that the HPE Private Cloud AI
"Small - Expanded" configuration meets their needs. They now need to generate a final, quotable Bill of Materials (BOM).
What is the most direct and efficient method for the sales team to create this BOM?

A) Use the standard HPE ProLiant DL380a Gen11 server template in OCA and add the GPUs and networking manually.
B) Send the output from the HPE Intelligent Configurator directly to the distribution partner for a quote.
C) Select the "HPE Private Cloud AI - Small - Expanded" Smart Template within HPE One Config Advanced (OCA).
D) Manually add each component (servers, GPUs, switches, cables, etc.) one by one into HPE One Config Advanced (OCA).

Solutions: