Examcollection HP HPE2-B08 Vce | Latest HPE2-B08 Exam Fee

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HP HPE2-B08 Exam Syllabus Topics:

Section	Weight	Objectives
Fundamental AI Concepts	28%	- Impact of AI on industries and infrastructure requirements - General AI concepts, applications and workloads
Solution Sizing and Configuration	17%	- Differences between configuration sizes and options - Using HPE Intelligent Configurator for sizing - Building configurations via One Config Advanced (OCA)
Customer Assessment and Solution Positioning	15%	- Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions
Software Components of HPE Private Cloud AI with NVIDIA	20%	- Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations
Infrastructure Components of HPE Private Cloud AI with NVIDIA	20%	- Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements

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New Examcollection HPE2-B08 Vce Pass Certify | Reliable Latest HPE2-B08 Exam Fee: HPE Private Cloud AI Solutions

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HPE Private Cloud AI Solutions Sample Questions (Q82-Q87):

NEW QUESTION # 82
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 NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
B. The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
C. The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
D. The NVIDIA peer memory driver has not been installed on the guest VM.
E. The network switches are not configured for lossless operation (e.g., PFC is disabled).

Answer: C,D,E

NEW QUESTION # 83
A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```

A. Use the HPE Private Cloud AI Large configuration with NVIDIA H100 GPUs to provide the necessary performance for the large-scale fine-tuning task.
B. Use HPE AI Essentials to manage the training cluster, providing features like experiment tracking and optimized resource scheduling for the fine-tuning job.
C. Recommend that the customer build their own solution from individual components to have maximum control.
D. Implement a Retrieval-Augmented Generation (RAG) architecture for the customer service chatbot to ensure it uses the latest, hourly-updated information.
E. Position Al-optimized HPE ProLiant DL servers at the edge for the fine-tuning workload to reduce data transfer costs.
F. Rely solely on public cloud services for the fine-tuning job to avoid capital expenditure on high- performance GPUs.

Answer: A,B,D

NEW QUESTION # 84
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 learning rate used for training was set too low.
C. The model has too few parameters to learn the features effectively.
D. The model is overfitting to the training data and cannot generalize to new data.

Answer: D

NEW QUESTION # 85
What is the primary function of hidden layers in a deep learning model?

A. To store the complete training dataset for real-time retrieval during inference.
B. To format the final output of the network into a human-readable prediction or classification.
C. To apply weights and transformations to the outputs of preceding layers, allowing the network to learn progressively more complex features.
D. To directly receive the initial, raw input data for the model.

Answer: C

NEW QUESTION # 86
An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?

A. It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
B. It will require the architect to switch to the "application capacity" sizing method.
C. It will have no effect, as the number of users is the primary factor.
D. It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.

Answer: A

NEW QUESTION # 87
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