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| Section | Weight | Objectives |
|---|
| Topic 1: Software Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations
|
| Topic 2: Solution Sizing and Configuration | 17% | - Differences between configuration sizes and options - Using HPE Intelligent Configurator for sizing - Building configurations via One Config Advanced (OCA)
|
| Topic 3: Fundamental AI Concepts | 28% | - General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements
|
| Topic 4: Customer Assessment and Solution Positioning | 15% | - Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions
|
| Topic 5: Infrastructure Components of HPE Private Cloud AI with NVIDIA | 20% | - Benefits of HPE and NVIDIA integrated infrastructure - Infrastructure capabilities for AI workload requirements
|
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HPE Private Cloud AI Solutions Sample Questions (Q32-Q37):
NEW QUESTION # 32
A customer has used the HPE Intelligent Configurator and determined that the HPE Private Cloud AI
"Small - Expanded" configuration meets their needs. They now need to generate a final, quotable Bill of Materials (BOM).
What is the most direct and efficient method for the sales team to create this BOM?
- A. Select the "HPE Private Cloud AI - Small - Expanded" Smart Template within HPE One Config Advanced (OCA).
- B. Manually add each component (servers, GPUs, switches, cables, etc.) one by one into HPE One Config Advanced (OCA).
- C. Use the standard HPE ProLiant DL380a Gen11 server template in OCA and add the GPUs and networking manually.
- D. Send the output from the HPE Intelligent Configurator directly to the distribution partner for a quote.
Answer: A
NEW QUESTION # 33
You are positioning HPE Private Cloud AI to a customer who is an "AI Pro" and wants to scale their generative AI efforts.
Which key capabilities of the solution would you emphasize to this customer? (Select all that apply.)
- A. Its ability to run on a single, low-power server for maximum efficiency.
- B. The inclusion of a full-stack, enterprise-supported software suite including NVIDIA AI Enterprise and HPE AI Essentials.
- C. Its turnkey nature, which simplifies deployment and allows their expert team to focus on building models rather than integrating infrastructure.
- D. A simple, web-based interface for beginners to learn about AI concepts.
- E. The option to choose a Large configuration with powerful NVIDIA H100 GPUs and a high-speed RoCE fabric for demanding fine-tuning workloads.
Answer: B,C,E
NEW QUESTION # 34
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?
- A. HPE Data Fabric
- B. Apache Airflow
- C. JupyterLab
- D. NVIDIA NIM (NVIDIA Inference Microservices)
Answer: D
NEW QUESTION # 35
An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?
- A. HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs
- B. HPE Alletra Storage MP with RoCE-capable network adapters
- C. HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
- D. HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge
Answer: D
NEW QUESTION # 36
An architect is comparing two different models for a text summarization task.
Model A: A Convolutional Neural Network (CNN)
*Model B: A Transformer-based model
Why is the Transformer-based model (Model B) fundamentally better suited for this task?
- A. Transformers use an attention mechanism to understand the contextual relationships between all words in the text, which is critical for summarization.
- B. CNNs require significantly more training data than Transformers.
- C. CNNs can only process images, not text.
- D. Transformers can be trained without GPUs, unlike CNNs.
Answer: A
NEW QUESTION # 37
......
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