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HP HPE2-B08 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Customer Assessment and Solution Positioning	15%	- Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases
Topic 2: Software Components of HPE Private Cloud AI with NVIDIA	20%	- Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack
Topic 3: Fundamental AI Concepts	28%	- General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements
Topic 4: Solution Sizing and Configuration	17%	- Building configurations via One Config Advanced (OCA) - Differences between configuration sizes and options - Using HPE Intelligent Configurator for sizing
Topic 5: Infrastructure Components of HPE Private Cloud AI with NVIDIA	20%	- Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements

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

NEW QUESTION # 71
A data analytics team is running workloads on an HPE Private Cloud AI solution. They observe that a data ingestion job is not meeting performance expectations, suspecting a CPU bottleneck. They believe the application is not correctly leveraging GPUDirect Storage (GDS), forcing data to be copied through the server's main memory before reaching the GPU.
Which are valid reasons why GDS might not be functioning correctly? (Choose 3.)

A. The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
B. The NVIDIA peer memory driver has not been installed on the guest VM.
C. The network switches are not configured for lossless operation (e.g., PFC is disabled).
D. The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
E. The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.

Answer: B,C,E

NEW QUESTION # 72
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 DL320 Gen11 server with NVIDIA L4 GPUs
B. HPE ProLiant DL380a Gen11 server with CPU-only configuration
C. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge but no GPUs
D. HPE ProLiant DL380a Gen11 server with NVIDIA L40S GPUs

Answer: D

NEW QUESTION # 73
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 Curator
C. NeMo Evaluator
D. NeMo Retriever
E. NeMo Guardrails

Answer: E

NEW QUESTION # 74
A customer wants to deploy a turnkey private cloud for a variety of generative AI workloads, including RAG-based chatbots and some model fine-tuning. One of their key IT stakeholders is the data engineer.
Which specific challenge for a data engineer is directly addressed by the HPE Data Fabric component within HPE Private Cloud AI?

A. The complexity of writing Python code in a Jupyter Notebook.
B. The high cost of NVIDIA AI Enterprise software licenses.
C. The difficulty of managing GPU cluster utilization for training jobs.
D. The effort required to create and manage data pipelines and unify diverse, siloed data sources (files, objects, tables) into a single accessible platform.

Answer: D

NEW QUESTION # 75
An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?

A. Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
B. Train a new LLM from scratch using only the company's policy documents to ensure data privacy.
C. Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
D. Use a pre-trained computer vision model to extract text from the policy documents.

Answer: A

NEW QUESTION # 76
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