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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements - Explain common AI use cases and how they map to workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Identify components of the HPE Private Cloud AI architecture |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Describe support resources and documentation - Explain how to work with HPE support services - Identify how to perform firmware and software updates |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Explain how to deploy and configure HPE Private Cloud AI components - Identify how to access and use HPE Private Cloud AI management interfaces - Identify the steps to configure the HPE Private Cloud AI environment - Describe how to validate the HPE Private Cloud AI installation - Describe the prerequisites for installing HPE Private Cloud AI |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe the tools and methods for managing HPE Private Cloud AI - Explain how to monitor HPE Private Cloud AI performance and health - Identify how to manage storage and data resources - Describe how to manage users and access control - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures |
HPE Private Cloud AI Solutions Sample Questions:
A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?
- A. Two separate solutions: a Small - Expanded for the RAG workload and a Large - Standard for the fine-tuning workload.
- B. Medium - Expanded, because it supports a high number of concurrent users for RAG.
- C. A custom-built solution using HPE ProLiant DL325 servers and NVIDIA L4 GPUs.
- D. Large - Expanded, because its NVIDIA H100 NVL GPUs and eight worker nodes are required for the intensive fine-tuning workload.
A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```
- A. Use HPE AI Essentials to manage the training cluster, providing features like experiment tracking and optimized resource scheduling for the fine-tuning job.
- B. Recommend that the customer build their own solution from individual components to have maximum control.
- C. Position Al-optimized HPE ProLiant DL servers at the edge for the fine-tuning workload to reduce data transfer costs.
- D. Use the HPE Private Cloud AI Large configuration with NVIDIA H100 GPUs to provide the necessary performance for the large-scale fine-tuning task.
- E. Rely solely on public cloud services for the fine-tuning job to avoid capital expenditure on high- performance GPUs.
- F. Implement a Retrieval-Augmented Generation (RAG) architecture for the customer service chatbot to ensure it uses the latest, hourly-updated information.
An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?
- A. 4x HPE ProLiant DL380a Gen11 servers with a total of 16x NVIDIA H100 NVL GPUs
- B. 3x HPE ProLiant DL325 Gen11 servers for management
- C. 2x HPE ProLiant DL380a Gen11 servers with a total of 8x NVIDIA L40S GPUs
- D. 1x HPE ProLiant DL380a Gen11 server with 4x NVIDIA L40S GPUs
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. AI Cybersecurity
- B. AI Recommender System
- C. Code Generation
- D. Document Chat
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 NIM (NVIDIA Inference Microservices)
- B. NVIDIA Triton Inference Server
- C. NVIDIA RAPIDS
- D. NVIDIA NeMo
Rod -
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