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| Section | Weight | Objectives |
|---|---|---|
| Solution Sizing and Configuration | 17% | - Differences between configuration sizes and options - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing |
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations |
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure |
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Customer Assessment and Solution Positioning | 15% | - Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases |
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NEW QUESTION # 50
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: D
NEW QUESTION # 51
A company is implementing a RAG-based chatbot using HPE Private Cloud AI. To ensure the chatbot provides safe and appropriate responses, the development team needs to implement guardrails to prevent it from discussing off-topic subjects and to block it from using harmful language.
Which specific toolkit within the NVIDIA NeMo framework is designed for this purpose?
```
NVIDIA NeMo Framework Components:
1. NeMo Curator
2. NeMo Customizer
3. NeMo Evaluator
4. NeMo Retriever
5. NeMo Guardrails
```
Answer: B
NEW QUESTION # 52
A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)
Answer: A,D,E
NEW QUESTION # 53
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: D
NEW QUESTION # 54
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: D
NEW QUESTION # 55
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