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| Section | Objectives |
|---|---|
| Topic 1: Deployment and Operations | - Monitoring and optimization - Deployment models for AI solutions - Lifecycle management of AI infrastructure |
| Topic 2: HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI |
| Topic 3: Data Management and Governance | - Data lifecycle management - Data governance and compliance |
| Topic 4: AI Infrastructure Design | - Compute and GPU considerations - Networking for AI workloads - Storage and data pipeline design |
| Topic 5: Security in Private Cloud AI | - Workload and data protection - Identity and access management |
| Topic 6: HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
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NEW QUESTION # 15
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?
Answer: B
NEW QUESTION # 16
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?
Answer: C
NEW QUESTION # 17
A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?
Answer: B
NEW QUESTION # 18
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.)
Answer: A,C
NEW QUESTION # 19
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)
Answer: B,E
NEW QUESTION # 20
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