Exam Name: HPE Private Cloud AI Solutions
Certification Provider: HP
Corresponding Certification: Hybrid Cloud
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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Supporting HPE Private Cloud AI Solutions | 20% | - Identify how to perform firmware and software updates - Describe support resources and documentation - Describe capacity planning and optimization best practices - Explain how to work with HPE support services |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Identify components of the HPE Private Cloud AI architecture - Explain common AI use cases and how they map to workloads - 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 |
| Topic 3: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Explain how to monitor HPE Private Cloud AI performance and health - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI - Describe how to manage users and access control - Identify how to manage storage and data resources - Identify troubleshooting procedures and common issues |
| Topic 4: 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 |
HPE Private Cloud AI Solutions Sample Questions:
A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)
- A. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
- B. The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
- C. The key component is the HPE ProLiant DL325 server in the management plane.
- D. The solution requires a Medium configuration.
- E. The solution requires a Large configuration.
Correct Answer: B,E 🗳️
A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer's requirements? (Select all that apply.)
```
Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
```
- A. It is managed exclusively through a command-line interface (CLI) for maximum control.
- B. It relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.
- C. It supports GPUDirect Storage (GDS) over a high-speed NVMe-oF (RoCE) network, enabling a direct, low-latency path between storage and GPUs.
- D. It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
- E. It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
Correct Answer: C,E 🗳️
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 recurrent layer
- B. An output layer with a softmax function
- C. A convolutional layer
- D. A dense (fully connected) layer
Correct Answer: C 🗳️
During a discovery call, a customer from a telecommunications company explains their primary goal:
"We need to analyze network traffic patterns in real-time to detect anomalies that could indicate a security threat or a network outage." Which key use case for HPE Private Cloud AI does this represent?
- A. Code Generation
- B. AI Recommender System
- C. AI Cybersecurity
- D. Document Chat
Correct Answer: C 🗳️
A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```
- A. NVIDIA Triton Inference Server
- B. NVIDIA RAPIDS
- C. NVIDIA NIM (NVIDIA Inference Microservices)
- D. NVIDIA NeMo
Correct Answer: D 🗳️
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