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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) Apply stricter content moderation and safety guardrails to the model's output.
D) Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
E) Retrain the model from scratch using a much larger and more diverse public dataset.

2. An architect has used the HPE Intelligent Configurator and determined that a "Medium - Standard" configuration is required.
When creating the final quote in One Config Advanced (OCA), what is the mandatory prerequisite that the customer is responsible for providing for the solution to be valid?

A) A subscription to the NVIDIA NGC catalog for downloading AI models.
B) A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud Foundation (VCF).
C) A list of all end-users who will access the system.
D) A rack elevation diagram for the data center.

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

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

5. A retail customer wants to implement an AI-powered recommender system to personalize product suggestions on their e-commerce website. They are an 'Early AI user' and need a full-stack solution that simplifies deployment and management.
Which HPE AI solution is most appropriate for this use case?

A) HPE Cray systems
B) HPE Private Cloud AI with NVIDIA
C) HPE AI Services - Transformation Workshop
D) AI-optimized HPE ProLiant DL servers deployed individually

Solutions: