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| Section | Weight | Objectives |
|---|---|---|
| Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements |
| Customer Assessment and Solution Positioning | 15% | - Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases |
| Solution Sizing and Configuration | 17% | - Building configurations via One Config Advanced (OCA) - Differences between configuration sizes and options - Using HPE Intelligent Configurator for sizing |
| Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations |
| Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure |
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NEW QUESTION # 39
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?
Answer: D
NEW QUESTION # 40
After an architect selects the "HPE Private Cloud AI - Large - Expanded" Smart Template in OCA, they see it includes a significant number of "HPE Factory Express Complex Unit of SVC" services.
What is the purpose of these bundled services?
Answer: D
NEW QUESTION # 41
A customer wants to enhance their existing Large Language Model (LLM) to provide more accurate and contextually relevant answers based on a proprietary, rapidly changing knowledge base of legal documents. They are considering two approaches: fine-tuning and Retrieval-Augmented Generation (RAG).
Review the data flow diagram for the proposed RAG implementation:
```
User Query -> [Query Encoder] -> Vector DB Search -> [Retrieved Documents] --+
|
+-> [LLM Prompt] -> LLM -> Response
```
Based on the diagram and the scenario, which statement accurately identifies a primary advantage of the RAG approach for this customer?
Answer: D
NEW QUESTION # 42
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: C
NEW QUESTION # 43
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: C
NEW QUESTION # 44
......
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