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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. After using the HPE Intelligent Configurator, an architect reviews the output for a proposed HPE Private Cloud AI solution.
In addition to the BOM, what other critical information does the tool provide that is essential for site readiness planning? (Select all that apply.)

A) The estimated power consumption in kW per rack.
B) The total weight of the solution for data center floor loading considerations.
C) A sample Python script for running the first inference job.
D) A list of recommended AI models for the specified use case.
E) A list of recommended HPE education courses for the IT staff.

2. An architect is assessing a customer's workloads to position the correct HPE Private Cloud AI configuration. The customer has a single, massive task: training a foundational vision model from scratch using a petabyte-scale image dataset. The project is expected to take months and requires the absolute maximum compute performance available.
Which AI workload type does this represent, and which HPE solution is most appropriate?

A) Workload: Edge Computing; Solution: HPE ProLiant DL320 server
B) Workload: AI Inferencing; Solution: AI-optimized HPE ProLiant DL servers
C) Workload: AI Model Training; Solution: HPE Cray Systems
D) Workload: RAG; Solution: HPE Private Cloud AI - Medium configuration

3. 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) Train a new LLM from scratch using only the company's policy documents to ensure data privacy.
C) Use a pre-trained computer vision model to extract text from the policy documents.
D) Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.

4. An IT director for a regional retail chain tells you they are "doing AI." Upon further questioning, you learn they have one data scientist who has built a single, experimental sales forecasting model as a proof-of-concept (PoC). The project lacks clear KPIs for success and there is no formal strategy for how to productionize it or what to do next.
How would you classify this customer's AI maturity level?

A) AI Pro
B) AI Beginner
C) Deployer of AI at scale
D) AI Curious

5. A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?

A) Large - Expanded, because its NVIDIA H100 NVL GPUs and eight worker nodes are required for the intensive fine-tuning workload.
B) A custom-built solution using HPE ProLiant DL325 servers and NVIDIA L4 GPUs.
C) Two separate solutions: a Small - Expanded for the RAG workload and a Large - Standard for the fine-tuning workload.
D) Medium - Expanded, because it supports a high number of concurrent users for RAG.

Solutions: