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HP HPE2-B08 Exam Syllabus Topics:

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

2. A data analytics team is running workloads on an HPE Private Cloud AI solution. They observe that a data ingestion job is not meeting performance expectations, suspecting a CPU bottleneck. They believe the application is not correctly leveraging GPUDirect Storage (GDS), forcing data to be copied through the server's main memory before reaching the GPU.
Which are valid reasons why GDS might not be functioning correctly? (Choose 3.)

A) The network switches are not configured for lossless operation (e.g., PFC is disabled).
B) The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
C) The NVIDIA peer memory driver has not been installed on the guest VM.
D) The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
E) The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.

3. A customer is using the NVIDIA NeMo framework within HPE Private Cloud AI to build a custom generative AI application. They need to fine-tune a foundation model using a proprietary dataset. They also want to ensure the final application does not produce toxic content or veer into off-topic conversations.
Which specific toolkits within the NeMo framework should they use to achieve these two distinct goals?
(Choose 2.)

A) NeMo Customizer
B) NeMo Retriever
C) NeMo Evaluator
D) NeMo Guardrails
E) NeMo Curator

4. 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 model is underfitting due to an insufficient number of training epochs.
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 learning rate used for training was set too low.

5. After using the HPE Intelligent Configurator, an architect reviews the output for a proposed HPE Private Cloud AI solution.
In addition to the BOM, what other critical information does the tool provide that is essential for site readiness planning? (Select all that apply.)

A) A sample Python script for running the first inference job.
B) A list of recommended HPE education courses for the IT staff.
C) A list of recommended AI models for the specified use case.
D) The total weight of the solution for data center floor loading considerations.
E) The estimated power consumption in kW per rack.

Solutions: