HPE Private Cloud AI Solutions : HPE2-B08

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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. A retail customer wants to implement an AI-powered recommender system to personalize product suggestions on their e-commerce website. They are an 'Early AI user' and need a full-stack solution that simplifies deployment and management.
Which HPE AI solution is most appropriate for this use case?

A) HPE Cray systems
B) HPE Private Cloud AI with NVIDIA
C) AI-optimized HPE ProLiant DL servers deployed individually
D) HPE AI Services - Transformation Workshop

2. A customer is using the NVIDIA NeMo framework within HPE Private Cloud AI to build a custom generative AI application. They need to fine-tune a foundation model using a proprietary dataset. They also want to ensure the final application does not produce toxic content or veer into off-topic conversations.
Which specific toolkits within the NeMo framework should they use to achieve these two distinct goals?
(Choose 2.)

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

3. A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?

A) The Large configuration does not support Retrieval-Augmented Generation (RAG).
B) The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
C) The Medium configuration has more storage capacity than the Large configuration.
D) The Medium configuration is the only one that includes the HPE AI Essentials software.

4. An architect is in a discovery call with a customer who describes their project: "Our primary goal is to take our massive, proprietary dataset of chemical compound interactions and continuously update our foundational AI model's internal parameters to create a new, specialized model for drug discovery. This process runs 24/7 on a large GPU cluster." How should the architect classify this primary AI workload?

A) Edge Computing
B) AI Inferencing
C) Retrieval-Augmented Generation (RAG)
D) AI Model Training / Fine-tuning

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

Solutions: