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HP HPE2-B08 Exam Syllabus Topics:

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

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HPE Private Cloud AI Solutions Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are positioning HPE Private Cloud AI to a customer who is an "AI Pro" and wants to scale their generative AI efforts.
Which key capabilities of the solution would you emphasize to this customer? (Select all that apply.)

A. A simple, web-based interface for beginners to learn about AI concepts.
B. Its turnkey nature, which simplifies deployment and allows their expert team to focus on building models rather than integrating infrastructure.
C. The option to choose a Large configuration with powerful NVIDIA H100 GPUs and a high-speed RoCE fabric for demanding fine-tuning workloads.
D. The inclusion of a full-stack, enterprise-supported software suite including NVIDIA AI Enterprise and HPE AI Essentials.
E. Its ability to run on a single, low-power server for maximum efficiency.

Answer: B,C,D

NEW QUESTION # 68
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 NVIDIA peer memory driver has not been installed on the guest VM.
B. The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
C. The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
D. The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
E. The network switches are not configured for lossless operation (e.g., PFC is disabled).

Answer: A,D,E

NEW QUESTION # 69
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. Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
B. Apply stricter content moderation and safety guardrails to the model's output.
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: A,C

NEW QUESTION # 70
An AI application is being developed to analyze financial time-series data to predict stock market trends. The model needs to understand long-term dependencies and the sequence in which data points occur.
Which neural network architecture is specifically designed to handle sequential data like text or time- series?

A. K-Means Clustering Algorithm
B. Transformer model
C. Convolutional Neural Network (CNN)
D. A fully connected, feedforward neural network

Answer: B

NEW QUESTION # 71
A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?

A. Both the data scientists and the operations team would use the HPE Intelligent Configurator to manage the models.
B. The data scientists would primarily use tools within HPE AI Essentials (like JupyterLab and MLFlow) for model development, while the operations team would use NVIDIA NIMs to deploy the finished models.
C. The data scientists would use NVIDIA NIMs to train the models, and the operations team would use HPE Data Fabric to serve them.
D. The entire process for all stakeholders is managed through the server's iLO interface.

Answer: B

NEW QUESTION # 72
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