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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?

A) A model development and experimentation workload.
B) A classic AI inferencing workload.
C) A RAG (Retrieval-Augmented Generation) inferencing workload.
D) A large-scale model training workload.

2. An architect is building a final configuration for an HPE Private Cloud AI solution using One Config Advanced (OCA). After selecting the "HPE Private Cloud AI - Medium Expanded" Smart Template, they review the generated Bill of Materials (BOM).
Which components are characteristic of the Medium Expanded configuration that the architect should expect to see in the OCA-generated BOM? (Select all that apply.)

A) 2x NVIDIA SN4700M Switches
B) 16x NVIDIA L40S GPUs
C) 2x HPE Aruba Networking CX 6300M Switches
D) 16x NVIDIA H100 NVL GPUs
E) 1x HPE ProLiant DL380a Gen11 Server
F) 4x HPE ProLiant DL380a Gen11 Servers

3. A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?

A) Apache Airflow
B) JupyterLab
C) NVIDIA NIM (NVIDIA Inference Microservices)
D) HPE Data Fabric

4. A customer wants a single platform to handle the following workloads:
* Real-time fraud detection using a trained model (Inference).
* A chatbot for internal support that uses a corporate knowledge base (RAG).
* Quarterly updates to their custom logistics model (Fine-tuning).
The customer is an 'AI Pro' looking for a turnkey, on-premises cloud experience.
Which HPE solution is designed to handle this mix of inference, RAG, and fine-tuning workloads?

A) HPE GreenLake for File Storage
B) HPE Private Cloud AI with NVIDIA
C) A dedicated HPE Cray system
D) A standalone cluster of AI-optimized HPE ProLiant servers

5. An architect is meeting with a prospective customer to determine the right HPE AI solution. The customer provides the following information about their situation.
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- AI Status: No formal AI strategy. One successful PoC for theft prevention using computer vision is running at a single store.
- Goal: Wants to explore creating a "digital twin" of their supply chain for simulation, but has no clear KPIs.
- Team: Two data scientists, one ML engineer.
- Infrastructure: Ad-hoc use of public cloud for the PoC; no standardized tech stack.
```
Based on this profile, which AI maturity level and corresponding HPE solution should the architect initially position? (Choose 2.)

A) Lead with HPE Cray systems for the digital twin simulation.
B) The customer is an 'AI Pro'.
C) The customer is a 'Deployer of AI at scale'.
D) Lead with HPE Private Cloud AI with NVIDIA.
E) The customer is an 'Early AI user'.

Solutions: