HP HPE2-B08 Practice Exams For Self-Assessment (Web-Based And Desktop)

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HP HPE2-B08 Exam Syllabus Topics:

SectionObjectives
AI Infrastructure Design- Compute and GPU considerations
- Networking for AI workloads
- Storage and data pipeline design
HPE GreenLake for AI Solutions- GreenLake architecture and services
- Consumption-based IT model for AI
HPE Private Cloud AI Fundamentals- Overview of private cloud AI concepts
- Core AI workload characteristics
Data Management and Governance- Data lifecycle management
- Data governance and compliance
Deployment and Operations- Deployment models for AI solutions
- Monitoring and optimization
- Lifecycle management of AI infrastructure
Security in Private Cloud AI- Identity and access management
- Workload and data protection

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HPE Private Cloud AI Solutions Sample Questions (Q14-Q19):

NEW QUESTION # 14
A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```

Answer: C


NEW QUESTION # 15
The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG

Answer: A


NEW QUESTION # 16
An enterprise is designing a solution for training a large, custom Convolutional Neural Network (CNN) for a new computer vision application. Their data science team has determined that the training process will need to be distributed across multiple GPUs to be completed in a reasonable timeframe. The training process involves intensive matrix multiplication operations.
The architect is specifying components from the HPE Private Cloud AI solution.
Which infrastructure components are critical for accelerating this specific distributed training workload?
(Select all that apply.)
```
Workload Analysis:
- AI Model: Large Convolutional Neural Network (CNN)
- Task: Distributed Training
- Key Operation: Intensive matrix multiplication
```

Answer: C,E


NEW QUESTION # 17
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.)

Answer: C,D


NEW QUESTION # 18
You are positioning HPE Private Cloud AI to a customer who is an "AI Pro" and wants to scale their generative AI efforts.
Which key capabilities of the solution would you emphasize to this customer? (Select all that apply.)

Answer: A,B,E


NEW QUESTION # 19
......

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