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| Section | Weight | Objectives |
|---|---|---|
| Architecting HPE Private Cloud AI Solutions | 20% | - 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 - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain common AI use cases and how they map to workloads |
| 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 - Identify how to manage storage and data resources - Describe how to manage users and access control - Explain backup and recovery procedures |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Describe support resources and documentation - Explain how to work with HPE support services - Identify how to perform firmware and software updates |
| 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 how to validate the HPE Private Cloud AI installation - Describe the prerequisites for installing HPE Private Cloud AI |
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NEW QUESTION # 44
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
```
Answer: A,E
NEW QUESTION # 45
An architect is selecting the appropriate HPE Private Cloud AI configuration for a customer. The customer's primary workload is fine-tuning a 70B parameter model, a task that is extremely sensitive to compute latency and requires the highest possible performance for multi-node distributed training.
Which specific infrastructure components included in the "Large" configuration of HPE Private Cloud AI are essential for meeting this high-performance training requirement? (Select all that apply.)
Answer: C,D,E
NEW QUESTION # 46
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: D
NEW QUESTION # 47
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"?
Answer: A
NEW QUESTION # 48
What is the primary advantage of using a Smart Template in OCA for HPE Private Cloud AI versus manually configuring a similar set of hardware?
Answer: B
NEW QUESTION # 49
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