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| Section | Objectives |
|---|
| HPE GreenLake for AI Solutions | - Consumption-based IT model for AI - GreenLake architecture and services
|
| Security in Private Cloud AI | - Workload and data protection - Identity and access management
|
| Data Management and Governance | - Data governance and compliance - Data lifecycle management
|
| HPE Private Cloud AI Fundamentals | - Core AI workload characteristics - Overview of private cloud AI concepts
|
| Deployment and Operations | - Monitoring and optimization - Deployment models for AI solutions - Lifecycle management of AI infrastructure
|
| AI Infrastructure Design | - Compute and GPU considerations - Storage and data pipeline design - Networking for AI workloads
|
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HPE Private Cloud AI Solutions Sample Questions (Q75-Q80):
NEW QUESTION # 75
An architect is designing a distributed training solution using HPE Private Cloud AI. The solution involves multiple HPE ProLiant DL380a servers, each with four NVIDIA H100 GPUs.
Review the logical network topology:
```
[HPE ProLiant Server 1] \
- GPU 0 <--> GPU 1 (Internal)
- GPU 2 <--> GPU 3 (Internal)
\
[NVIDIA Spectrum Switch (RoCE)] <--> [HPE GreenLake for File Storage]
/
[HPE ProLiant Server 2] /
- GPU 0 <--> GPU 1 (Internal)
- GPU 2 <--> GPU 3 (Internal)
```
Which interconnect technology facilitates the highest-bandwidth communication path labeled "(Internal)" for scaling the training job across the GPUs within a single server?
- A. PCI Express (PCIe) Gen5
- B. NVIDIA NVLink Bridge
- C. GPUDirect Storage (GDS)
- D. RDMA over Converged Ethernet (RoCE)
Answer: B
NEW QUESTION # 76
A customer wants to build a configuration in One Config Advanced (OCA) for the HPE Private Cloud AI "Large - Standard" solution.
Which key components should the architect expect the Smart Template to include in the Bill of Materials (BOM)? (Choose 2.)
- A. 8 worker nodes.
- B. HPE Cray compute nodes.
- C. HPE ProLiant DL380a Gen11 servers with NVIDIA H100 NVL GPUs.
- D. 4 worker nodes.
- E. HPE ProLiant DL380a Gen11 servers with NVIDIA L40S GPUs.
Answer: C,D
NEW QUESTION # 77
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 difficulty of managing GPU cluster utilization for training jobs.
- C. The complexity of writing Python code in a Jupyter Notebook.
- D. The high cost of NVIDIA AI Enterprise software licenses.
Answer: A
NEW QUESTION # 78
A customer at the 'Early AI User' maturity stage wants to begin using generative AI. Their primary concern is the complexity of deploying and managing the AI software stack. They are looking for a solution that accelerates their journey by providing a pre-integrated, enterprise-supported software platform for both development and deployment.
How do the software components of HPE Private Cloud AI address this customer's main challenge?
(Select all that apply.)
```
Customer Profile:
- Maturity: Early AI User
- Main Challenge: Complexity of AI software stack
- Goal: Accelerate adoption of generative AI
```
- A. NVIDIA NIMs drastically simplify the deployment of pre-trained models into production-ready microservices.
- B. NVIDIAAI Enterprise provides enterprise-grade support, security, and API stability for the entire AI software stack.
- C. The solution requires the customer to manually integrate all software components, which promotes learning.
- D. HPE AI Essentials provides a unified, self-service platform with curated open-source tools, eliminating integration complexity.
- E. The software stack is limited to only HPE-developed tools to ensure a single point of contact.
Answer: A,B,D
NEW QUESTION # 79
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 model architecture that includes pooling layers to reduce spatial dimensions
- B. A model architecture that includes convolutional layers for feature extraction
- C. A Convolutional Neural Network (CNN)
- D. A transformer-based model designed for Natural Language Processing (NLP)
- E. A basic, fully connected Artificial Neural Network (ANN)
Answer: A,B,C
NEW QUESTION # 80
......
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