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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain common AI use cases and how they map to workloads - Identify components of the HPE Private Cloud AI architecture - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe the AI/ML lifecycle and data pipeline requirements |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Describe how to validate the HPE Private Cloud AI installation - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces - Explain how to deploy and configure HPE Private Cloud AI components |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe the tools and methods for managing HPE Private Cloud AI - Identify how to manage storage and data resources - Explain backup and recovery procedures - Explain how to monitor HPE Private Cloud AI performance and health - Identify troubleshooting procedures and common issues - Describe how to manage users and access control |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe support resources and documentation - Describe capacity planning and optimization best practices - Identify how to perform firmware and software updates - Explain how to work with HPE support services |
HPE Private Cloud AI Solutions Sample Questions:
A customer is building an application to classify images of defective products on a manufacturing line.
Which type of neural network layer is essential for this model to automatically learn and identify visual features like edges, corners, and textures in the images?
- A. A convolutional layer
- B. A recurrent layer
- C. An output layer with a softmax function
- D. A dense (fully connected) layer
Correct Answer: A 🗳️
A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?
- A. An HPE AI Services - Transformation Workshop
- B. AI-optimized HPE ProLiant DL servers
- C. NVIDIA DGX Systems
- D. HPE Private Cloud AI with NVIDIA
- E. HPE Cray systems
Correct Answer: B 🗳️
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 Medium configuration is the only one that includes the HPE AI Essentials software.
- B. The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
- C. The Large configuration does not support Retrieval-Augmented Generation (RAG).
- D. The Medium configuration has more storage capacity than the Large configuration.
Correct Answer: B 🗳️
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 model is being automatically fine-tuned by the NIM using the TensorRT framework.
- B. The NIM container has failed because it requires an FP32 profile to run.
- C. The underlying GPU supports the Hopper architecture, and NIM has optimized the model using a lower precision format for faster performance.
- D. The NIM container is running on a CPU-only node and is emulating a GPU.
Correct Answer: C 🗳️
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 have no effect, as the number of users is the primary factor.
- B. It will require the architect to switch to the "application capacity" sizing method.
- C. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.
- D. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
Correct Answer: D 🗳️
