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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - 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 - Describe the prerequisites for installing HPE Private Cloud AI |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain common AI use cases and how they map to workloads - Describe the AI/ML lifecycle and data pipeline requirements - Explain the HPE Private Cloud AI sizing and configuration guidelines - Identify components of the HPE Private Cloud AI architecture |
| Topic 3: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify how to manage storage and data resources - 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 |
| Topic 4: Supporting HPE Private Cloud AI Solutions | 20% | - Describe support resources and documentation - Explain how to work with HPE support services - Identify how to perform firmware and software updates - Describe capacity planning and optimization best practices |
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NEW QUESTION # 78
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,D
NEW QUESTION # 79
An architect is in a discovery call with a customer who describes their project: "Our primary goal is to take our massive, proprietary dataset of chemical compound interactions and continuously update our foundational AI model's internal parameters to create a new, specialized model for drug discovery. This process runs 24/7 on a large GPU cluster." How should the architect classify this primary AI workload?
Answer: C
NEW QUESTION # 80
A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?
Answer: A
NEW QUESTION # 81
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: C
NEW QUESTION # 82
A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?
Answer: B
NEW QUESTION # 83
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