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HP HPE2-B08 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Supporting HPE Private Cloud AI Solutions | 20% | - Describe capacity planning and optimization best practices - Identify how to perform firmware and software updates - Describe support resources and documentation - Explain how to work with HPE support services |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Identify components of the HPE Private Cloud AI architecture - Explain common AI use cases and how they map to workloads - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe the AI/ML lifecycle and data pipeline requirements |
| Topic 3: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe how to manage users and access control - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures - Identify how to manage storage and data resources - Explain how to monitor HPE Private Cloud AI performance and health - Describe the tools and methods for managing HPE Private Cloud AI |
| Topic 4: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Describe how to validate the HPE Private Cloud AI installation - Identify the steps to configure the HPE Private Cloud AI environment - Explain how to deploy and configure HPE Private Cloud AI components - Identify how to access and use HPE Private Cloud AI management interfaces |
HPE Private Cloud AI Solutions Sample Questions:
A customer is planning to deploy an HPE Private Cloud AI solution. Their IT department has provided a worksheet detailing the workload requirements. The solution architect needs to select the appropriate configuration size.
Review the customer's requirements:
```
- Primary Workload: Fine-tuning a 70-billion parameter foundation model
- Secondary Workload: AI Inferencing with RAG
- User Base: 50 developers during tuning; 300 concurrent users for inference
- Data Sensitivity: High (must remain on-premises)
- Power/Cooling: Standard data center air cooling available
```
Which HPE Private Cloud AI configuration is required to meet these demands, and what is the key component that justifies this choice? (Choose 2.)
- A. The solution requires a Medium configuration.
- B. The key component is the HPE ProLiant DL325 server in the management plane.
- C. The solution requires a Large configuration.
- D. The key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
- E. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
A customer in the media and entertainment industry wants a solution to accelerate the creation of 3D animations and renderings. Their artists need to be able to generate complex visual AI content quickly.
The company has a mature AI practice and requires a powerful, data center-based solution.
Which HPE ProLiant server and NVIDIA GPU combination is purpose-built for these generative visual AI workloads?
- A. HPE ProLiant DL380a Gen11 server with NVIDIA L40S GPUs
- B. HPE ProLiant DL380a Gen11 server with CPU-only configuration
- C. HPE ProLiant DL320 Gen11 server with NVIDIA L4 GPUs
- D. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge but no GPUs
A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)
- A. A Convolutional Neural Network (CNN)
- B. A model architecture that includes pooling layers to reduce spatial dimensions
- C. A model architecture that includes convolutional layers for feature extraction
- D. A basic, fully connected Artificial Neural Network (ANN)
- E. A transformer-based model designed for Natural Language Processing (NLP)
A customer wants to deploy a turnkey private cloud for a variety of generative AI workloads, including RAG-based chatbots and some model fine-tuning. One of their key IT stakeholders is the data engineer.
Which specific challenge for a data engineer is directly addressed by the HPE Data Fabric component within HPE Private Cloud AI?
- A. The effort required to create and manage data pipelines and unify diverse, siloed data sources (files, objects, tables) into a single accessible platform.
- B. The complexity of writing Python code in a Jupyter Notebook.
- C. The difficulty of managing GPU cluster utilization for training jobs.
- D. The high cost of NVIDIA AI Enterprise software licenses.
A financial services firm is building a fraud detection application on HPE Private Cloud AI. The application needs to process a continuous stream of transaction data from multiple sources in real time.
The data science team requires a robust platform to build, manage, and execute the complex data pipelines needed to feed the AI model.
Which pre-integrated open-source tool within HPE AI Essentials is the industry standard for orchestrating and managing these complex data workflows and pipelines?
- A. Apache Airflow
- B. Prometheus
- C. Kyverno
- D. JupyterLab
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