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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe how to manage users and access control - Identify how to manage storage and data resources - Identify troubleshooting procedures and common issues - Explain how to monitor HPE Private Cloud AI performance and health - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Explain common AI use cases and how they map to workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe how HPE Private Cloud AI supports AI/ML workloads |
| Topic 3: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Explain how to deploy and configure HPE Private Cloud AI components - Describe how to validate the HPE Private Cloud AI installation - Identify how to access and use HPE Private Cloud AI management interfaces - Describe the prerequisites for installing HPE Private Cloud AI - Identify the steps to configure the HPE Private Cloud AI environment |
| Topic 4: Supporting HPE Private Cloud AI Solutions | 20% | - Identify how to perform firmware and software updates - Describe support resources and documentation - Describe capacity planning and optimization best practices - Explain how to work with HPE support services |
It is similar to the HPE2-B08 desktop-based software, with all the elements of the desktop practice exam. This mock exam can be accessed from any browser and does not require installation. The HPE Private Cloud AI Solutions (HPE2-B08) questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based HPE Private Cloud AI Solutions (HPE2-B08) practice test immediately through any operating system and browsers.
NEW QUESTION # 48
What is the primary function of hidden layers in a deep learning model?
Answer: D
NEW QUESTION # 49
A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?
Answer: B
NEW QUESTION # 50
A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?
Answer: D
NEW QUESTION # 51
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: D
NEW QUESTION # 52
An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?
Answer: D
NEW QUESTION # 53
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