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HP HPE2-B08 Exam Syllabus Topics:

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

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HPE Private Cloud AI Solutions Sample Questions (Q30-Q35):

NEW QUESTION # 30
After using the HPE Intelligent Configurator, an architect reviews the output for a proposed HPE Private Cloud AI solution.
In addition to the BOM, what other critical information does the tool provide that is essential for site readiness planning? (Select all that apply.)

A. A list of recommended HPE education courses for the IT staff.
B. The total weight of the solution for data center floor loading considerations.
C. A list of recommended AI models for the specified use case.
D. The estimated power consumption in kW per rack.
E. A sample Python script for running the first inference job.

Answer: B,C,D

NEW QUESTION # 31
A customer wants to build a configuration in One Config Advanced (OCA) for the HPE Private Cloud AI "Large - Standard" solution.
Which key components should the architect expect the Smart Template to include in the Bill of Materials (BOM)? (Choose 2.)

A. HPE Cray compute nodes.
B. HPE ProLiant DL380a Gen11 servers with NVIDIA L40S GPUs.
C. 4 worker nodes.
D. 8 worker nodes.
E. HPE ProLiant DL380a Gen11 servers with NVIDIA H100 NVL GPUs.

Answer: C,E

NEW QUESTION # 32
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 Customizer
B. NeMo Guardrails
C. NeMo Evaluator
D. NeMo Curator
E. NeMo Retriever

Answer: B

NEW QUESTION # 33
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)

A. Multi-Instance GPU (MIG)
B. Remote Direct Memory Access (RDMA)
C. NVLink Bridge
D. GPUDirect Storage (GDS)
E. Confidential Computing

Answer: A,E

NEW QUESTION # 34
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. Apply stricter content moderation and safety guardrails to the model's output.
B. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
C. Reduce the "temperature" setting during inference to make the model's output less random and more focused.
D. Increase the number of hidden layers in the model's architecture.
E. Retrain the model from scratch using a much larger and more diverse public dataset.

Answer: B,C

NEW QUESTION # 35
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