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HP HPE2-B08 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deployment and Operations | - Monitoring and optimization - Deployment models for AI solutions - Lifecycle management of AI infrastructure |
| Topic 2: HPE GreenLake for AI Solutions | - Consumption-based IT model for AI - GreenLake architecture and services |
| Topic 3: AI Infrastructure Design | - Storage and data pipeline design - Compute and GPU considerations - Networking for AI workloads |
| Topic 4: Data Management and Governance | - Data lifecycle management - Data governance and compliance |
| Topic 5: Security in Private Cloud AI | - Workload and data protection - Identity and access management |
| Topic 6: HPE Private Cloud AI Fundamentals | - Overview of private cloud AI concepts - Core AI workload characteristics |
HPE Private Cloud AI Solutions Sample Questions:
An architect is assessing a customer's workloads to position the correct HPE Private Cloud AI configuration. The customer has a single, massive task: training a foundational vision model from scratch using a petabyte-scale image dataset. The project is expected to take months and requires the absolute maximum compute performance available.
Which AI workload type does this represent, and which HPE solution is most appropriate?
- A. Workload: RAG; Solution: HPE Private Cloud AI - Medium configuration
- B. Workload: AI Inferencing; Solution: AI-optimized HPE ProLiant DL servers
- C. Workload: Edge Computing; Solution: HPE ProLiant DL320 server
- D. Workload: AI Model Training; Solution: HPE Cray Systems
Correct Answer: D 🗳️
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
```
- A. NVIDIA GPUs featuring multiple Tensor Cores.
- B. HPE ProLiant DL325 servers for the worker nodes.
- C. An NVIDIA NVLink Bridge to connect the GPUs.
- D. The HPE Data Fabric software component.
- E. HPE GreenLake for File Storage with standard NFS over TCP/IP.
Correct Answer: A,C 🗳️
An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?
- A. 1x HPE ProLiant DL380a Gen11 server with 4x NVIDIA L40S GPUs
- B. 4x HPE ProLiant DL380a Gen11 servers with a total of 16x NVIDIA H100 NVL GPUs
- C. 3x HPE ProLiant DL325 Gen11 servers for management
- D. 2x HPE ProLiant DL380a Gen11 servers with a total of 8x NVIDIA L40S GPUs
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. Input Layer
- B. Output Layer
- C. The connections between the Input and Hidden layers
- D. Hidden Layer
Correct Answer: B 🗳️
In a Natural Language Processing (NLP) task, what is the role of a tokenizer?
- A. To convert a sentence of raw text into a sequence of numerical tokens that a model can process.
- B. To apply a final activation function to determine the output class.
- C. To train the neural network using backpropagation.
- D. To encrypt the input text to ensure data privacy during model training.
Correct Answer: A 🗳️
