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| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain common AI use cases and how they map to workloads - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Explain the HPE Private Cloud AI sizing and configuration guidelines |
| Supporting HPE Private Cloud AI Solutions | 20% | - Explain how to work with HPE support services - Describe support resources and documentation - Identify how to perform firmware and software updates - Describe capacity planning and optimization best practices |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Identify the steps to configure the HPE Private Cloud AI environment - Describe how to validate the HPE Private Cloud AI installation - 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 how to manage users and access control - Explain how to monitor HPE Private Cloud AI performance and health - Identify how to manage storage and data resources - Explain backup and recovery procedures - Identify troubleshooting procedures and common issues - Describe the tools and methods for managing HPE Private Cloud AI |
Question 1
You are in a meeting with the VP of Manufacturing for a large industrial company. She describes their AI program as follows:
"We have a formal Center of Excellence for AI that reports directly to the CTO. Our strategy is to scale our successful predictive maintenance models across all 25 of our global plants. We have standardized on a consistent cloud and data governance strategy for all AI projects and have clear ROI targets for reducing downtime." Based on this description, which AI maturity level best describes this customer?
A. AI Pro
B. AI Beginner
C. AI Curious
D. Early AI user
Question 2
A customer in the public sector wants to use AI to analyze live video feeds from city-wide cameras to automate public safety tasks like detecting traffic accidents or identifying security threats in real-time.
This is their first major AI initiative.
Which AI use case does this scenario represent?
A. Computer Vision / Intelligent Video Analytics
B. Drug Discovery
C. Recommender Systems
D. Natural Language Processing (NLP)
Question 3
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 solution requires a Medium configuration.
D. The solution requires a Large configuration.
E. The key component is the HPE ProLiant DL325 server in the management plane.
Question 4
An enterprise is designing a solution for training a large, custom Convolutional Neural Network (CNN) for a new computer vision application. Their data science team has determined that the training process will need to be distributed across multiple GPUs to be completed in a reasonable timeframe. The training process involves intensive matrix multiplication operations.
The architect is specifying components from the HPE Private Cloud AI solution.
Which infrastructure components are critical for accelerating this specific distributed training workload?
(Select all that apply.)
```
Workload Analysis:
- AI Model: Large Convolutional Neural Network (CNN)
- Task: Distributed Training
- Key Operation: Intensive matrix multiplication
```
A. An NVIDIA NVLink Bridge to connect the GPUs.
B. NVIDIA GPUs featuring multiple Tensor Cores.
C. HPE ProLiant DL325 servers for the worker nodes.
D. HPE GreenLake for File Storage with standard NFS over TCP/IP.
E. The HPE Data Fabric software component.
Question 5
A customer wants to enhance their existing Large Language Model (LLM) to provide more accurate and contextually relevant answers based on a proprietary, rapidly changing knowledge base of legal documents. They are considering two approaches: fine-tuning and Retrieval-Augmented Generation (RAG).
Review the data flow diagram for the proposed RAG implementation:
```
User Query -> [Query Encoder] -> Vector DB Search -> [Retrieved Documents] --+
|
+-> [LLM Prompt] -> LLM -> Response
```
Based on the diagram and the scenario, which statement accurately identifies a primary advantage of the RAG approach for this customer?
A. RAG permanently modifies the LLM's internal weights to specialize in legal terminology.
B. RAG requires retraining the LLM whenever a new legal document is added to the knowledge base.
C. RAG allows the LLM to access the most current legal documents at inference time without daily retraining.
D. RAG reduces the need for a vector database by directly integrating documents into the model.
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: B,D | Question 4 Answer: A,B | Question 5 Answer: C |
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