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HP HPE2-B08 Exam Syllabus Topics:

SectionWeightObjectives
Infrastructure Components of HPE Private Cloud AI with NVIDIA20%- Infrastructure capabilities for AI workload requirements
- Benefits of HPE and NVIDIA integrated infrastructure
Customer Assessment and Solution Positioning15%- Assess AI maturity, workload characteristics and use cases
- Position appropriate HPE AI solutions
Software Components of HPE Private Cloud AI with NVIDIA20%- Benefits of HPE and NVIDIA software stack
- Software functions supporting AI operations
Solution Sizing and Configuration17%- Using HPE Intelligent Configurator for sizing
- Building configurations via One Config Advanced (OCA)
- Differences between configuration sizes and options
Fundamental AI Concepts28%- Impact of AI on industries and infrastructure requirements
- General AI concepts, applications and workloads

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HPE Private Cloud AI Solutions Sample Questions (Q31-Q36):

NEW QUESTION # 31
An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team's data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)

Answer: A,B


NEW QUESTION # 32
A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```

Answer: C,D,F


NEW QUESTION # 33
A customer wants a single platform to handle the following workloads:
* Real-time fraud detection using a trained model (Inference).
* A chatbot for internal support that uses a corporate knowledge base (RAG).
* Quarterly updates to their custom logistics model (Fine-tuning).
The customer is an 'AI Pro' looking for a turnkey, on-premises cloud experience.
Which HPE solution is designed to handle this mix of inference, RAG, and fine-tuning workloads?

Answer: B


NEW QUESTION # 34
A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```

Answer: B


NEW QUESTION # 35
The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG

Answer: C


NEW QUESTION # 36
......

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