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A customer in the media and entertainment industry wants a solution to accelerate the creation of 3D animations and renderings. Their artists need to be able to generate complex visual AI content quickly.
The company has a mature AI practice and requires a powerful, data center-based solution.
Which HPE ProLiant server and NVIDIA GPU combination is purpose-built for these generative visual AI workloads?

• A. HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
• B. HPE ProLiant DL380a Gen11 server with CPU-only configuration
• C. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge but no GPUs
• D. HPE ProLiant DL380a Gen11 server with NVIDIA L40S GPUs

A financial services firm is building a fraud detection application on HPE Private Cloud AI. The application needs to process a continuous stream of transaction data from multiple sources in real time.
The data science team requires a robust platform to build, manage, and execute the complex data pipelines needed to feed the AI model.
Which pre-integrated open-source tool within HPE AI Essentials is the industry standard for orchestrating and managing these complex data workflows and pipelines?

• A. JupyterLab
• B. Kyverno
• C. Prometheus
• D. Apache Airflow

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?

• A. The learning rate used for training was set too low.
• B. The model has too few parameters to learn the features effectively.
• C. The model is overfitting to the training data and cannot generalize to new data.
• D. The model is underfitting due to an insufficient number of training epochs.

A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```

• A. NVIDIA RAPIDS
• B. NVIDIA NIM (NVIDIA Inference Microservices)
• C. NVIDIA NeMo
• D. NVIDIA Triton Inference Server

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?

• A. HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
• B. HPE Cray system with NVIDIA Grace Hopper Superchips
• C. HPE ProLiant DL384 Gen12 server with NVIDIA H200 NVL GPUs
• D. HPE ProLiant DL380a server with NVIDIA H100 NVL GPUs