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HP HPE2-B08 Exam Syllabus Topics:

SectionObjectives
Topic 1: HPE Private Cloud AI Fundamentals- Overview of private cloud AI concepts
- Core AI workload characteristics
Topic 2: HPE GreenLake for AI Solutions- Consumption-based IT model for AI
- GreenLake architecture and services
Topic 3: Security in Private Cloud AI- Identity and access management
- Workload and data protection
Topic 4: Data Management and Governance- Data lifecycle management
- Data governance and compliance
Topic 5: AI Infrastructure Design- Storage and data pipeline design
- Networking for AI workloads
- Compute and GPU considerations
Topic 6: Deployment and Operations- Monitoring and optimization
- Deployment models for AI solutions
- Lifecycle management of AI infrastructure

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HP HPE2-B08 Questions - Pass Exam With Ease (2026)

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HPE Private Cloud AI Solutions Sample Questions (Q74-Q79):

NEW QUESTION # 74
A customer is using the NVIDIA NeMo framework within HPE Private Cloud AI to build a custom generative AI application. They need to fine-tune a foundation model using a proprietary dataset. They also want to ensure the final application does not produce toxic content or veer into off-topic conversations.
Which specific toolkits within the NeMo framework should they use to achieve these two distinct goals?
(Choose 2.)

Answer: A,D


NEW QUESTION # 75
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: A,B,F


NEW QUESTION # 76
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: C


NEW QUESTION # 77
An 'AI Pro' customer wants to deploy a solution to create 'digital twins' of their manufacturing equipment for predictive maintenance simulations. This workload requires significant GPU power for both the simulation and the underlying AI model.
Which HPE solution should be positioned as the primary platform for this advanced, data-center based workload?

Answer: D


NEW QUESTION # 78
An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?

Answer: B


NEW QUESTION # 79
......

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