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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations |
| Topic 2: Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Topic 3: Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements |
| Topic 4: Solution Sizing and Configuration | 17% | - Differences between configuration sizes and options - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing |
| Topic 5: Customer Assessment and Solution Positioning | 15% | - Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions |
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NEW QUESTION # 15
An architect is sizing an HPE Private Cloud AI solution. The customer plans to deploy a generative AI application for 150 concurrent users that requires Retrieval-Augmented Generation (RAG).
The architect enters the following into the HPE Intelligent Configurator:
```
- Use case: Text Generation
- Number of users: 100-250
- RAG: Yes
- Model: Llama 2 13B (tool default for this use case)
```
Based on the provided inputs, which HPE Private Cloud AI configuration will the HPE Intelligent Configurator most likely recommend?
Answer: B
NEW QUESTION # 16
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 # 17
A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?
Answer: D
NEW QUESTION # 18
A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?
Answer: C
NEW QUESTION # 19
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 # 20
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