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HP HPE2-B08 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing and Operating HPE Private Cloud AI Solutions	30%	- Describe the tools and methods for managing HPE Private Cloud AI - Describe how to manage users and access control - Explain backup and recovery procedures - Identify troubleshooting procedures and common issues - Explain how to monitor HPE Private Cloud AI performance and health - Identify how to manage storage and data resources
Topic 2: Installing and Configuring HPE Private Cloud AI Solutions	30%	- Explain how to deploy and configure HPE Private Cloud AI components - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces - Describe how to validate the HPE Private Cloud AI installation - Describe the prerequisites for installing HPE Private Cloud AI
Topic 3: Architecting HPE Private Cloud AI Solutions	20%	- Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Explain the HPE Private Cloud AI sizing and configuration guidelines - Explain common AI use cases and how they map to workloads
Topic 4: Supporting HPE Private Cloud AI Solutions	20%	- Explain how to work with HPE support services - Describe capacity planning and optimization best practices - Identify how to perform firmware and software updates - Describe support resources and documentation

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HPE Private Cloud AI Solutions HPE2-B08 Prüfungsfragen mit Lösungen (Q45-Q50):

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

Antwort: A,B

46. Frage
A customer needs a solution for AI inferencing at the edge for a computer vision application. They have determined a Small configuration of HPE Private Cloud AI is sufficient. The solution also requires several value-added services, including an AI transformation workshop and a data design service.
How should an architect attach these services in One Config Advanced (OCA)?

A. The services are automatically included in the Smart Template and require no action.
B. The services can be selected and added to the configuration as optional line items within OCA.
C. The architect must open a support ticket with HPE to have the services added to the quote.
D. The services must be quoted separately from the hardware and software configuration.

Antwort: B

47. Frage
An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?

A. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
B. It will require the architect to switch to the "application capacity" sizing method.
C. It will have no effect, as the number of users is the primary factor.
D. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.

Antwort: A

48. Frage
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?

A. Individual HPE ProLiant DL320 Gen11 servers at the edge.
B. A standard data warehouse solution without GPU acceleration.
C. HPE Private Cloud AI with NVIDIA.
D. The customer's existing VDI environment.

Antwort: C

49. Frage
A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)

A. The solution requires a Large configuration.
B. The solution requires a Medium configuration.
C. The key component is the HPE ProLiant DL325 server in the management plane.
D. The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
E. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.

Antwort: A,D

50. Frage
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