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HP HPE2-B08 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: HPE GreenLake for AI Solutions | - Consumption-based IT model for AI - GreenLake architecture and services |
| Topic 2: Security in Private Cloud AI | - Workload and data protection - Identity and access management |
| Topic 3: Data Management and Governance | - Data governance and compliance - Data lifecycle management |
| Topic 4: Deployment and Operations | - Lifecycle management of AI infrastructure - Deployment models for AI solutions - Monitoring and optimization |
| Topic 5: AI Infrastructure Design | - Networking for AI workloads - Storage and data pipeline design - Compute and GPU considerations |
| Topic 6: HPE Private Cloud AI Fundamentals | - Core AI workload characteristics - Overview of private cloud AI concepts |
HPE Private Cloud AI Solutions Sample Questions:
An architect is selecting the appropriate HPE Private Cloud AI configuration for a customer. The customer's primary workload is fine-tuning a 70B parameter model, a task that is extremely sensitive to compute latency and requires the highest possible performance for multi-node distributed training.
Which specific infrastructure components included in the "Large" configuration of HPE Private Cloud AI are essential for meeting this high-performance training requirement? (Select all that apply.)
- A. NVIDIA NVLink Bridge
- B. NVIDIA L40S GPUs
- C. HPE Aruba Networking CX 6300M switches
- D. NVIDIA SN4700M switches supporting 200GbE RoCE
- E. NVIDIA H100 NVL GPUs
Correct Answer: A,D,E 🗳️
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 key component is the NVIDIA H100 NVL GPU, designed for large-scale training and fine-tuning.
- B. The key component is the HPE ProLiant DL325 server in the management plane.
- C. The solution requires a Medium configuration.
- D. The solution requires a Large configuration.
- E. The key component is the NVIDIA L40S GPU, which is optimized for RAG workloads.
Correct Answer: A,D 🗳️
The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG
- A. 1-b, 2-c, 3-a
- B. 1-a, 2-b, 3-c
- C. 1-c, 2-a, 3-b
- D. 1-b, 2-a, 3-c
Correct Answer: A 🗳️
What is the primary purpose of using RDMA over Converged Ethernet (RoCE) in the AI interconnect fabric of an HPE Private Cloud AI solution?
- A. To allow direct memory-to-memory data transfers between nodes, bypassing the CPU kernel and reducing latency.
- B. To encrypt data in transit between compute nodes and storage.
- C. To guarantee packet delivery over lossy internet connections using the TCP protocol.
- D. To partition a single physical network adapter into multiple virtual adapters for different tenants.
Correct Answer: A 🗳️
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?
- A. A RAG (Retrieval-Augmented Generation) inferencing workload.
- B. A classic AI inferencing workload.
- C. A model development and experimentation workload.
- D. A large-scale model training workload.
Correct Answer: A 🗳️
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