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HPE Private Cloud AI Solutions Sample Questions:

1. An architect is presenting to a customer who is an 'Early AI User'. The customer is unsure about the differences between their two main project ideas.
Project 1: Create an internal chatbot that can answer HR policy questions by accessing the live employee handbook stored on a shared drive.
Project 2: Teach a general-purpose chatbot to adopt the company's formal communication style and tone for drafting official press releases.
How should the architect categorize the primary AI task for each project? (Choose 2.)

A) Project 2 is a classic inferencing workload with no model customization.
B) Project 1 is a RAG (Retrieval-Augmented Generation) workload.
C) Project 2 is a fine-tuning workload.
D) Project 1 is a model training workload.

2. A customer needs a solution for AI inferencing at the edge for a computer vision application. They have determined a Small configuration of HPE Private Cloud AI is sufficient. The solution also requires several value-added services, including an AI transformation workshop and a data design service.
How should an architect attach these services in One Config Advanced (OCA)?

A) The services can be selected and added to the configuration as optional line items within OCA.
B) The services are automatically included in the Smart Template and require no action.
C) The services must be quoted separately from the hardware and software configuration.
D) The architect must open a support ticket with HPE to have the services added to the quote.

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

4. 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?

A) NVIDIA Triton Inference Server
B) NVIDIA NIM (NVIDIA Inference Microservices)
C) HPE Machine Learning Development Environment
D) NVIDIA RAPIDS

5. An AI application is being developed to analyze financial time-series data to predict stock market trends. The model needs to understand long-term dependencies and the sequence in which data points occur.
Which neural network architecture is specifically designed to handle sequential data like text or time- series?

A) K-Means Clustering Algorithm
B) A fully connected, feedforward neural network
C) Transformer model
D) Convolutional Neural Network (CNN)

Solutions: