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HP HPE2-B08 Exam Syllabus Topics:

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

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HPE Private Cloud AI Solutions Sample Questions (Q16-Q21):

NEW QUESTION # 16
A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?

A. HPE Cray systems
B. AI-optimized HPE ProLiant DL servers
C. HPE Private Cloud AI with NVIDIA, Large configuration
D. HPE GreenLake for File Storage

Answer: A

NEW QUESTION # 17
When a developer deploys an AI model using an NVIDIA Inference Microservice (NIM) on HPE Private Cloud AI, the NIM automatically detects the underlying NVIDIA GPU hardware.
The developer sees the following status message upon deployment:
```
NIM Status:
- Model: mistral-7b-instruct-v0.2
- Status: Running
- Engine Profile: Selected 'TensorRT-LLM FP8' for optimal latency on detected Hopper-class GPU.
```
What does the selection of the 'TensorRT-LLM FP8' engine profile indicate?

A. The NIM container is running on a CPU-only node and is emulating a GPU.
B. The underlying GPU supports the Hopper architecture, and NIM has optimized the model using a lower precision format for faster performance.
C. The NIM container has failed because it requires an FP32 profile to run.
D. The model is being automatically fine-tuned by the NIM using the TensorRT framework.

Answer: B

NEW QUESTION # 18
An architect is positioning an HPE Private Cloud AI solution to a customer who is an "AI Pro." The customer's CIO is the key decision maker.
Which benefits of the solution would be most compelling to this stakeholder? (Choose 2.)

A. Access to a command-line interface for the network switches.
B. The ability to write Python code in a Jupyter Notebook.
C. A predictable, cloud-like operational expense model and a clear return on investment (ROI) by accelerating time-to-value for AI projects.
D. The specific version of the CUDA driver used by the NVIDIA GPUs.
E. The ability to use NVIDIA NIMs to drastically simplify and standardize model deployment, reducing operational complexity and cost.

Answer: C,E

NEW QUESTION # 19
An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?

A. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
B. It will require the architect to switch to the "application capacity" sizing method.
C. It will have no effect, as the number of users is the primary factor.
D. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.

Answer: D

NEW QUESTION # 20
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. Increase the number of hidden layers in the model's architecture.
C. Retrain the model from scratch using a much larger and more diverse public dataset.
D. Apply stricter content moderation and safety guardrails to the model's output.
E. Reduce the "temperature" setting during inference to make the model's output less random and more focused.

Answer: A,E

NEW QUESTION # 21
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