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HPE Private Cloud AI Solutions Sample Questions:

1. An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?

A) HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
B) HPE Alletra Storage MP with RoCE-capable network adapters
C) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge
D) HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs

2. 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?

A) Early AI user
B) AI Beginner
C) AI Curious
D) AI Pro

3. A company has developed a custom fraud detection model. Their data science team wants to deploy this model into production for other applications to use. They want to avoid the complexity of manually configuring the serving environment, optimizing for hardware, and creating a scalable API endpoint.
How does using an NVIDIA Inference Microservice (NIM) within HPE Private Cloud AI simplify this process?

A) NIM automatically fine-tunes the model on new data to improve its accuracy.
B) NIM provides a framework that packages the model into an optimized, ready-to-deploy container with a standard API.
C) NIM provisions the underlying Kubernetes cluster and physical servers.
D) NIM directly connects to the raw data sources to perform data preparation and cleaning.

4. A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)

A) The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
B) The key component is the HPE ProLiant DL325 server in the management plane.
C) The solution requires a Medium configuration.
D) The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
E) The solution requires a Large configuration.

5. A customer in the public sector wants to use AI to analyze live video feeds from city-wide cameras to automate public safety tasks like detecting traffic accidents or identifying security threats in real-time.
This is their first major AI initiative.
Which AI use case does this scenario represent?

A) Drug Discovery
B) Recommender Systems
C) Natural Language Processing (NLP)
D) Computer Vision / Intelligent Video Analytics

Solutions: