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HP HPE2-B08 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Installing and Configuring HPE Private Cloud AI Solutions30%- Describe the prerequisites for installing HPE Private Cloud AI
- Describe how to validate the HPE Private Cloud AI installation
- Identify how to access and use HPE Private Cloud AI management interfaces
- Explain how to deploy and configure HPE Private Cloud AI components
- Identify the steps to configure the HPE Private Cloud AI environment
Topic 2: Managing and Operating HPE Private Cloud AI Solutions30%- Describe the tools and methods for managing HPE Private Cloud AI
- Explain backup and recovery procedures
- Identify how to manage storage and data resources
- Identify troubleshooting procedures and common issues
- Describe how to manage users and access control
- Explain how to monitor HPE Private Cloud AI performance and health
Topic 3: Supporting HPE Private Cloud AI Solutions20%- Identify how to perform firmware and software updates
- Describe support resources and documentation
- Explain how to work with HPE support services
- Describe capacity planning and optimization best practices
Topic 4: Architecting HPE Private Cloud AI Solutions20%- Identify components of the HPE Private Cloud AI architecture
- Describe how HPE Private Cloud AI supports AI/ML workloads
- Describe the AI/ML lifecycle and data pipeline requirements
- Explain common AI use cases and how they map to workloads
- Explain the HPE Private Cloud AI sizing and configuration guidelines

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

NEW QUESTION # 31
A sales professional is preparing a quote for an HPE Private Cloud AI solution in OCA and needs to ensure the correct support services are attached.
What is the default level of support included with all components when using the "Flexible services" option in the Smart Template?

Answer: A


NEW QUESTION # 32
A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?

Answer: B


NEW QUESTION # 33
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.)

Answer: C,E


NEW QUESTION # 34
An architect is comparing two different models for a text summarization task.
Model A: A Convolutional Neural Network (CNN)
*Model B: A Transformer-based model
Why is the Transformer-based model (Model B) fundamentally better suited for this task?

Answer: A


NEW QUESTION # 35
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 # 36
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