HPE2-B08 試験問題を無料オンラインアクセス

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 has too few parameters to learn the features effectively.
• C.The model is underfitting due to an insufficient number of training epochs.
• D.The learning rate used for training was set too low.

A customer is building an application to classify images of defective products on a manufacturing line.
Which type of neural network layer is essential for this model to automatically learn and identify visual features like edges, corners, and textures in the images?

• A.An output layer with a softmax function
• B.A recurrent layer
• C.A dense (fully connected) layer
• D.A convolutional layer

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 Evaluator
• B.NeMo Retriever
• C.NeMo Customizer
• D.NeMo Guardrails
• E.NeMo Curator

An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team's data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)

• A.HPE Data Fabric
• B.HPE AI Essentials
• C.Kyverno
• D.HPE GreenLake for File Storage
• E.NVIDIA NIM (NVIDIA Inference Microservices)

An architect is working with a customer who has been identified as an 'AI Pro'. They have a mature AI practice and are looking to scale their efforts in creating 'digital twin' simulations for automotive prototypes. This involves complex, graphics-intensive AI workloads. The customer wants a full-stack, turnkey private cloud solution to accelerate this initiative.
What is the most appropriate HPE solution to position for this customer?

• A.A custom solution built on individual HPE ProLiant servers.
• B.A set of AI-optimized HPE ProLiant DL servers for edge deployment.
• C.An HPE AI Services - Transformation Workshop to redefine their goals.
• D.HPE Private Cloud AI with NVIDIA.