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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. 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.
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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 a 'Deployer of AI at scale'.
C) Lead with HPE Private Cloud AI with NVIDIA.
D) The customer is an 'Early AI user'.
E) The customer is an 'AI Pro'.

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

3. A customer explains that their data engineers are spending too much time managing disparate data pipelines with a complex set of open-source tools. They are an 'Early AI User' trying to standardize their approach.
Which value proposition of HPE Private Cloud AI directly addresses this specific stakeholder's pain point?

A) It can be deployed in a colocation facility through HPE GreenLake.
B) It offers a choice of NVIDIA GPUs to accelerate model training.
C) Its management plane uses three redundant HPE ProLiant DL325 servers for high availability.
D) It includes HPE AI Essentials, which provides a unified platform with pre-integrated data pipeline and workflow tools like Apache Airflow and Spark.

4. An architect is presenting to a customer who is an 'Early AI User'. The customer is unsure about the differences between their two main project ideas.
Project 1: Create an internal chatbot that can answer HR policy questions by accessing the live employee handbook stored on a shared drive.
Project 2: Teach a general-purpose chatbot to adopt the company's formal communication style and tone for drafting official press releases.
How should the architect categorize the primary AI task for each project? (Choose 2.)

A) Project 1 is a RAG (Retrieval-Augmented Generation) workload.
B) Project 1 is a model training workload.
C) Project 2 is a fine-tuning workload.
D) Project 2 is a classic inferencing workload with no model customization.

5. 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 Medium configuration is the only one that includes the HPE AI Essentials software.
C) The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
D) The Medium configuration has more storage capacity than the Large configuration.

Solutions: