HPE Private Cloud AI Solutions 認定 HPE2-B08 試験問題:

1. You are in a meeting with the VP of Manufacturing for a large industrial company. She describes their AI program as follows:
"We have a formal Center of Excellence for AI that reports directly to the CTO. Our strategy is to scale our successful predictive maintenance models across all 25 of our global plants. We have standardized on a consistent cloud and data governance strategy for all AI projects and have clear ROI targets for reducing downtime." Based on this description, which AI maturity level best describes this customer?

A) AI Curious
B) Early AI user
C) AI Beginner
D) AI Pro

2. What is the primary function of hidden layers in a deep learning model?

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

3. An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```

A) A Retrieval-Augmented Generation (RAG) framework to query the vector database.
B) A process for daily fine-tuning of the base LLM on all new research papers.
C) A data ingestion and embedding pipeline to process and add new research to the vector database.
D) An edge-optimized server for model deployment in individual hospital rooms.
E) A pre-trained, general-purpose Large Language Model (LLM).
F) A vector database to store embeddings of the medical research papers.

4. An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)

A) NVLink Bridge
B) Multi-Instance GPU (MIG)
C) GPUDirect Storage (GDS)
D) Confidential Computing
E) Remote Direct Memory Access (RDMA)

5. When a developer deploys an AI model using an NVIDIA Inference Microservice (NIM) on HPE Private Cloud AI, the NIM automatically detects the underlying NVIDIA GPU hardware.
The developer sees the following status message upon deployment:
```
NIM Status:
- Model: mistral-7b-instruct-v0.2
- Status: Running
- Engine Profile: Selected 'TensorRT-LLM FP8' for optimal latency on detected Hopper-class GPU.
```
What does the selection of the 'TensorRT-LLM FP8' engine profile indicate?

A) The model is being automatically fine-tuned by the NIM using the TensorRT framework.
B) The underlying GPU supports the Hopper architecture, and NIM has optimized the model using a lower precision format for faster performance.
C) The NIM container has failed because it requires an FP32 profile to run.
D) The NIM container is running on a CPU-only node and is emulating a GPU.

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