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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 |
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements |
| Customer Assessment and Solution Positioning | 15% | - Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases |
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack |
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NEW QUESTION # 30
An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?
Answer: B
NEW QUESTION # 31
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: B
NEW QUESTION # 32
A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?
Answer: D
NEW QUESTION # 33
A sales professional is preparing a quote for an HPE Private Cloud AI solution in OCA and needs to ensure the correct support services are attached.
What is the default level of support included with all components when using the "Flexible services" option in the Smart Template?
Answer: D
NEW QUESTION # 34
An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team's data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)
Answer: C,E
NEW QUESTION # 35
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