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| Section | Objectives |
|---|
| Topic 1: Security in Private Cloud AI | - Identity and access management - Workload and data protection
|
| Topic 2: HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI
|
| Topic 3: HPE Private Cloud AI Fundamentals | - Core AI workload characteristics - Overview of private cloud AI concepts
|
| Topic 4: Data Management and Governance | - Data governance and compliance - Data lifecycle management
|
| Topic 5: Deployment and Operations | - Lifecycle management of AI infrastructure - Deployment models for AI solutions - Monitoring and optimization
|
| Topic 6: AI Infrastructure Design | - Storage and data pipeline design - Compute and GPU considerations - Networking for AI workloads
|
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HPE Private Cloud AI Solutions Sample Questions (Q83-Q88):
NEW QUESTION # 83
An architect is explaining the structure of a simple Artificial Neural Network (ANN) to a client.
Review the following diagram of the network:
```
Input Layer Hidden Layer Output Layer
(Node) -- (Node) -- (Node)
(Node) / (Node) -\- (Node)
(Node) --/ (Node) / (Node)
```
Which component of the network is responsible for producing the final prediction, such as classifying an image as either a "cat" or a "dog"?
- A. Hidden Layer
- B. Input Layer
- C. Output Layer
- D. The connections between the Input and Hidden layers
Answer: C
NEW QUESTION # 84
A customer is using the HPE Intelligent Configurator to determine the correct size for an HPE Private Cloud AI solution. Their primary use case is a text generation chatbot for internal HR support.
What is the MOST critical piece of information the customer must provide to the sizing tool to get an accurate recommendation for the number and type of GPUs required?
- A. The number of ML engineers who will maintain the model.
- B. The physical location of the data center where the solution will be deployed.
- C. The brand of networking switches used in their existing data center fabric.
- D. The estimated number of concurrent users who will be querying the chatbot.
Answer: D
NEW QUESTION # 85
An architect is meeting with a prospective customer to determine the right HPE AI solution. The customer provides the following information about their situation.
```
- AI Status: No formal AI strategy. One successful PoC for theft prevention using computer vision is running at a single store.
- Goal: Wants to explore creating a "digital twin" of their supply chain for simulation, but has no clear KPIs.
- Team: Two data scientists, one ML engineer.
- Infrastructure: Ad-hoc use of public cloud for the PoC; no standardized tech stack.
```
Based on this profile, which AI maturity level and corresponding HPE solution should the architect initially position? (Choose 2.)
- A. The customer is an 'AI Pro'.
- B. The customer is an 'Early AI user'.
- C. The customer is a 'Deployer of AI at scale'.
- D. Lead with HPE Cray systems for the digital twin simulation.
- E. Lead with HPE Private Cloud AI with NVIDIA.
Answer: B,E
NEW QUESTION # 86
An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```
- A. An edge-optimized server for model deployment in individual hospital rooms.
- B. A process for daily fine-tuning of the base LLM on all new research papers.
- C. A data ingestion and embedding pipeline to process and add new research to the vector database.
- D. A vector database to store embeddings of the medical research papers.
- E. A Retrieval-Augmented Generation (RAG) framework to query the vector database.
- F. A pre-trained, general-purpose Large Language Model (LLM).
Answer: C,D,E,F
NEW QUESTION # 87
A customer wants a single platform to handle the following workloads:
* Real-time fraud detection using a trained model (Inference).
* A chatbot for internal support that uses a corporate knowledge base (RAG).
* Quarterly updates to their custom logistics model (Fine-tuning).
The customer is an 'AI Pro' looking for a turnkey, on-premises cloud experience.
Which HPE solution is designed to handle this mix of inference, RAG, and fine-tuning workloads?
- A. A dedicated HPE Cray system
- B. HPE GreenLake for File Storage
- C. A standalone cluster of AI-optimized HPE ProLiant servers
- D. HPE Private Cloud AI with NVIDIA
Answer: D
NEW QUESTION # 88
......
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