HPE2-B08 Practice Questions | HPE2-B08 High Passing Score

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HP HPE2-B08 Exam Syllabus Topics:

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

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HPE Private Cloud AI Solutions Sample Questions (Q71-Q76):

NEW QUESTION # 71
A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?

A. HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
B. HPE ProLiant DL384 Gen12 server with NVIDIA H200 NVL GPUs
C. HPE Cray system with NVIDIA Grace Hopper Superchips
D. HPE ProLiant DL380a server with NVIDIA H100 NVL GPUs

Answer: A

NEW QUESTION # 72
A company is implementing a RAG-based chatbot using HPE Private Cloud AI. To ensure the chatbot provides safe and appropriate responses, the development team needs to implement guardrails to prevent it from discussing off-topic subjects and to block it from using harmful language.
Which specific toolkit within the NVIDIA NeMo framework is designed for this purpose?
```
NVIDIA NeMo Framework Components:
1. NeMo Curator
2. NeMo Customizer
3. NeMo Evaluator
4. NeMo Retriever
5. NeMo Guardrails
```

A. NeMo Evaluator
B. NeMo Customizer
C. NeMo Curator
D. NeMo Retriever
E. NeMo Guardrails

Answer: E

NEW QUESTION # 73
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 have no effect, as the number of users is the primary factor.
B. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
C. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
D. It will require the architect to switch to the "application capacity" sizing method.

Answer: C

NEW QUESTION # 74
An enterprise architecture team is debating the best method to adapt a general-purpose Large Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly changing knowledge base of product manuals and technical notes. Verifiability and traceability of the information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)

A. Use both RAG and fine-tuning for both use cases as they are always used together.
B. Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the model's behavior.
C. Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
D. Use Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
E. Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.

Answer: B,D

NEW QUESTION # 75
A customer in the media and entertainment industry wants a solution to accelerate the creation of 3D animations and renderings. Their artists need to be able to generate complex visual AI content quickly.
The company has a mature AI practice and requires a powerful, data center-based solution.
Which HPE ProLiant server and NVIDIA GPU combination is purpose-built for these generative visual AI workloads?

A. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge but no GPUs
B. HPE ProLiant DL380a Gen11 server with NVIDIA L40S GPUs
C. HPE ProLiant DL380a Gen11 server with CPU-only configuration
D. HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs

Answer: B

NEW QUESTION # 76
......

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