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HPE Private Cloud AI Solutions Sample Questions:

1. A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)

A) Increase the number of hidden layers in the model's architecture.
B) Retrain the model from scratch using a much larger and more diverse public dataset.
C) Reduce the "temperature" setting during inference to make the model's output less random and more focused.
D) Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
E) Apply stricter content moderation and safety guardrails to the model's output.

2. A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer's requirements? (Select all that apply.)
```
Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
```

A) It includes HPE Data Fabric, providing a global namespace across the worker nodes' local drives.
B) It uses a disaggregated architecture, allowing controller nodes (performance) and storage shelves (capacity) to be scaled separately.
C) It is managed exclusively through a command-line interface (CLI) for maximum control.
D) It supports GPUDirect Storage (GDS) over a high-speed NVMe-oF (RoCE) network, enabling a direct, low-latency path between storage and GPUs.
E) It relies on traditional hard disk drives (HDDs) for cost-effective capacity, with a small flash tier for caching.

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

4. An architect is comparing two different models for a text summarization task.
Model A: A Convolutional Neural Network (CNN)
*Model B: A Transformer-based model
Why is the Transformer-based model (Model B) fundamentally better suited for this task?

A) CNNs can only process images, not text.
B) CNNs require significantly more training data than Transformers.
C) Transformers use an attention mechanism to understand the contextual relationships between all words in the text, which is critical for summarization.
D) Transformers can be trained without GPUs, unlike CNNs.

5. A customer has selected the "HPE Private Cloud AI - Medium - Expanded" configuration.
Which of the following resource counts correctly describes this specific configuration?

A) 2 worker nodes, 8 total L40S GPUs, single rack
B) 4 worker nodes, 16 total L40S GPUs, single rack
C) 8 worker nodes, 32 total H100 NVL GPUs, dual rack
D) 4 worker nodes, 16 total H100 NVL GPUs, dual rack

Solutions: