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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Explain how to work with HPE support services - Describe support resources and documentation - Identify how to perform firmware and software updates |
| Topic 2: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - 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 - Describe the prerequisites for installing HPE Private Cloud AI - Describe how to validate the HPE Private Cloud AI installation |
| Topic 3: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe the tools and methods for managing HPE Private Cloud AI - Identify troubleshooting procedures and common issues - Explain how to monitor HPE Private Cloud AI performance and health - Explain backup and recovery procedures - Describe how to manage users and access control - Identify how to manage storage and data resources |
| Topic 4: Architecting HPE Private Cloud AI Solutions | 20% | - Identify components of the HPE Private Cloud AI architecture - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain common AI use cases and how they map to workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe the AI/ML lifecycle and data pipeline requirements |
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NEW QUESTION # 32
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?
Answer: A
NEW QUESTION # 33
A data science team has trained a deep learning model for image classification. While the model achieves 99.8% accuracy on the training dataset, its accuracy drops to only 75% on a new, unseen validation dataset.
The team provides the following training metrics:
```
- Training Epochs: 500
- Training Dataset Size: 1,000 images
- Model Parameters: 15 million
- Training Accuracy: 99.8%
- Validation Accuracy: 75.3%
```
What is the most likely cause of this performance discrepancy?
Answer: A
NEW QUESTION # 34
An architect has generated a configuration for HPE Private Cloud AI in OCA. The customer wants to know if they can use their existing data center network switches for the high-performance AI interconnect instead of the NVIDIA Spectrum switches included in the BOM.
What is the correct response?
Answer: A
NEW QUESTION # 35
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: B,C
NEW QUESTION # 36
A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?
Answer: B
NEW QUESTION # 37
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