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HP HPE2-B08 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Security in Private Cloud AI | - Identity and access management - Workload and data protection |
| HPE Private Cloud AI Fundamentals | - Core AI workload characteristics - Overview of private cloud AI concepts |
| 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 |
| AI Infrastructure Design | - Storage and data pipeline design - Compute and GPU considerations - Networking for AI workloads |
| HPE GreenLake for AI Solutions | - GreenLake architecture and services - Consumption-based IT model for AI |
HPE Private Cloud AI Solutions Sample Questions:
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 peer memory driver has not been installed on the guest VM.
- B. The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
- C. The network switches are not configured for lossless operation (e.g., PFC is disabled).
- D. The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
- E. The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
Correct Answer: A,C,D 🗳️
An architect is designing an AI solution for a financial services company that needs to build a chatbot.
The chatbot must answer customer queries using the company's latest internal policy documents, which are updated daily. The company has a limited budget and no data scientists available for a lengthy model retraining project.
Which approach should the architect recommend?
- A. Use a pre-trained computer vision model to extract text from the policy documents.
- B. Use a pre-trained model and implement a Retrieval-Augmented Generation (RAG) framework.
- C. Fine-tune a pre-trained model daily by retraining it on the entire set of policy documents.
- D. Train a new LLM from scratch using only the company's policy documents to ensure data privacy.
Correct Answer: B 🗳️
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 NIM container has failed because it requires an FP32 profile to run.
- B. The underlying GPU supports the Hopper architecture, and NIM has optimized the model using a lower precision format for faster performance.
- C. The model is being automatically fine-tuned by the NIM using the TensorRT framework.
- D. The NIM container is running on a CPU-only node and is emulating a GPU.
Correct Answer: B 🗳️
A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?
- A. AI-optimized HPE ProLiant DL servers
- B. HPE Private Cloud AI with NVIDIA
- C. HPE Cray systems
- D. An HPE AI Services - Transformation Workshop
- E. NVIDIA DGX Systems
Correct Answer: A 🗳️
An architect is explaining the structure of a simple Artificial Neural Network (ANN) to a client.
Review the following diagram of the network:
```
Input Layer Hidden Layer Output Layer
(Node) -- (Node) -- (Node)
(Node) / (Node) -\- (Node)
(Node) --/ (Node) / (Node)
```
Which component of the network is responsible for producing the final prediction, such as classifying an image as either a "cat" or a "dog"?
- A. The connections between the Input and Hidden layers
- B. Input Layer
- C. Output Layer
- D. Hidden Layer
Correct Answer: C 🗳️
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