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HP HPE2-B08 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Infrastructure Design- Networking for AI workloads
- Compute and GPU considerations
- Storage and data pipeline design
Topic 2: Security in Private Cloud AI- Workload and data protection
- Identity and access management
Topic 3: Deployment and Operations- Deployment models for AI solutions
- Lifecycle management of AI infrastructure
- Monitoring and optimization
Topic 4: HPE Private Cloud AI Fundamentals- Core AI workload characteristics
- Overview of private cloud AI concepts
Topic 5: Data Management and Governance- Data lifecycle management
- Data governance and compliance
Topic 6: HPE GreenLake for AI Solutions- Consumption-based IT model for AI
- GreenLake architecture and services

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HPE Private Cloud AI Solutions Sample Questions (Q28-Q33):

NEW QUESTION # 28
A customer's distributed AI training job is experiencing poor performance. An analysis reveals high network latency. The architect suspects a misconfiguration in the NVIDIA Spectrum switches that form the RoCE-based AI fabric.
They review the following configuration snippet from one of the switch ports connected to a compute node:
```
interface ethernet 1/1
description "Connection to PCAI-Node-1"
speed 200G
mtu 9216
switchport mode trunk
## Priority Flow Control (PFC) is disabled on this port
```
What is the most likely cause of the performance issue?

Answer: A


NEW QUESTION # 29
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)

Answer: C,E


NEW QUESTION # 30
A financial services company, currently at the 'AI Pro' maturity level, wants to build a private cloud solution to support two primary initiatives:
1. Initiative 1: Fine-tune a proprietary 70B parameter LLM for fraud detection, requiring maximum training performance.
2. Initiative 2: Deploy a customer-facing RAG-based chatbot for 500 concurrent users.
The customer wants a single, integrated solution that can handle both workloads efficiently. They have a new data center with ample power and cooling.
Which HPE Private Cloud AI configuration should the architect recommend?

Answer: B


NEW QUESTION # 31
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.)

Answer: A,B,D


NEW QUESTION # 32
A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?

Answer: A


NEW QUESTION # 33
......

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