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Exam
:
HPE2-B08
Title
: HPE Private Cloud AI
Solutions
https://www.passcert.com/HPE2-B08.html
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1.During a discovery call, a customer from a telecommunications company explains their primary goal:
"We need to analyze network traffic patterns in real-time to detect anomalies that could indicate a security
threat or a network outage."
Which key use case for HPE Private Cloud AI does this represent?
A. AI Cybersecurity
B. Code Generation
C. AI Recommender System
D. Document Chat
Answer: A
2.A data analytics team is running workloads on an HPE Private Cloud AI solution. They observe that a
data ingestion job is not meeting performance expectations, suspecting a CPU bottleneck. They believe
the application is not correctly leveraging GPUDirect Storage (GDS), forcing data to be copied through
the server's main memory before reaching the GPU.
Which are valid reasons why GDS might not be functioning correctly? (Choose 3.)
A. The network switches are not configured for lossless operation (e.g., PFC is disabled).
B. The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS
performance.
C. The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
D. The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
E. The NVIDIA peer memory driver has not been installed on the guest VM.
Answer: A, C, E
3.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. NVIDIA NIM (NVIDIA Inference Microservices)
B. HPE Data Fabric
C. Apache Airflow
D. JupyterLab
Answer: A
4.An architect has used the HPE Intelligent Configurator and determined that a "Medium - Standard"
configuration is required.
When creating the final quote in One Config Advanced (OCA), what is the mandatory prerequisite that the
customer is responsible for providing for the solution to be valid?
A. A valid, bring-your-own-license (BYOL) for VMware vSphere Foundation (VVF) or VMware Cloud
Foundation (VCF).
B. A rack elevation diagram for the data center.
C. A list of all end-users who will access the system.
D. A subscription to the NVIDIA NGC catalog for downloading AI models.
Answer: A
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5.A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG
application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this
customer?
A. The Large configuration does not support Retrieval-Augmented Generation (RAG).
B. The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this
specific inference-heavy workload.
C. The Medium configuration is the only one that includes the HPE AI Essentials software.
D. The Medium configuration has more storage capacity than the Large configuration.
Answer: B
6.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. Use the standard HPE ProLiant DL380a Gen11 server template in OCA and add the GPUs and
networking manually.
C. Manually add each component (servers, GPUs, switches, cables, etc.) one by one into HPE One
Config Advanced (OCA).
D. Send the output from the HPE Intelligent Configurator directly to the distribution partner for a quote.
Answer: A
7.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?
A. RAG requires retraining the LLM whenever a new legal document is added to the knowledge base.
B. RAG permanently modifies the LLM's internal weights to specialize in legal terminology.
C. RAG reduces the need for a vector database by directly integrating documents into the model.
D. RAG allows the LLM to access the most current legal documents at inference time without daily
retraining.
Answer: D
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8.A company has developed a custom fraud detection model. Their data science team wants to deploy
this model into production for other applications to use. They want to avoid the complexity of manually
configuring the serving environment, optimizing for hardware, and creating a scalable API endpoint.
How does using an NVIDIA Inference Microservice (NIM) within HPE Private Cloud AI simplify this
process?
A. NIM provisions the underlying Kubernetes cluster and physical servers.
B. NIM automatically fine-tunes the model on new data to improve its accuracy.
C. NIM directly connects to the raw data sources to perform data preparation and cleaning.
D. NIM provides a framework that packages the model into an optimized, ready-to-deploy container with a
standard API.
Answer: D
9.A customer in the public sector wants to use AI to analyze live video feeds from city-wide cameras to
automate public safety tasks like detecting traffic accidents or identifying security threats in real-time. This
is their first major AI initiative.
Which AI use case does this scenario represent?
A. Recommender Systems
B. Computer Vision / Intelligent Video Analytics
C. Natural Language Processing (NLP)
D. Drug Discovery
Answer: B
10.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 classic AI inferencing workload.
B. A model development and experimentation workload.
C. A RAG (Retrieval-Augmented Generation) inferencing workload.
D. A large-scale model training workload.
Answer: C
11.A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT
staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?
A. HPE Cray systems
B. HPE Private Cloud AI with NVIDIA
C. An HPE AI Services - Transformation Workshop
D. AI-optimized HPE ProLiant DL servers
E. NVIDIA DGX Systems
Answer: D
12.What is the primary advantage of using a Smart Template in OCA for HPE Private Cloud AI versus
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manually configuring a similar set of hardware?
A. The Smart Template guarantees the lowest possible price for the hardware.
B. The Smart Template provides a 90-day free trial of the entire solution.
C. The Smart Template allows the use of components from other vendors.
D. The Smart Template ensures all necessary and validated components, including specific cables, power
cords, and software SKUs, are included correctly.
Answer: D
13.A global logistics company is designing an enterprise-grade AI solution. The project has two main
goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation
model on the company's massive, confidential shipping dataset (20TB). This requires a secure,
high-performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot
must provide real-time shipment status and answer policy questions based on a knowledge base that is
updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they
want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```
A. Position Al-optimized HPE ProLiant DL servers at the edge for the fine-tuning workload to reduce data
transfer costs.
B. Implement a Retrieval-Augmented Generation (RAG) architecture for the customer service chatbot to
ensure it uses the latest, hourly-updated information.
C. Rely solely on public cloud services for the fine-tuning job to avoid capital expenditure on
high-performance GPUs.
D. Use the HPE Private Cloud AI Large configuration with NVIDIA H100 GPUs to provide the necessary
performance for the large-scale fine-tuning task.
E. Use HPE AI Essentials to manage the training cluster, providing features like experiment tracking and
optimized resource scheduling for the fine-tuning job.
F. Recommend that the customer build their own solution from individual components to have maximum
control.
Answer: B, D, E
14.An IT director for a regional retail chain tells you they are "doing AI." Upon further questioning, you
learn they have one data scientist who has built a single, experimental sales forecasting model as a
proof-of-concept (PoC). The project lacks clear KPIs for success and there is no formal strategy for how
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to productionize it or what to do next.
How would you classify this customer's AI maturity level?
A. Deployer of AI at scale
B. AI Curious
C. AI Pro
D. AI Beginner
Answer: D
15.An enterprise is designing a solution for training a large, custom Convolutional Neural Network (CNN)
for a new computer vision application. Their data science team has determined that the training process
will need to be distributed across multiple GPUs to be completed in a reasonable timeframe. The training
process involves intensive matrix multiplication operations.
The architect is specifying components from the HPE Private Cloud AI solution.
Which infrastructure components are critical for accelerating this specific distributed training workload?
(Select all that apply.)
```
Workload Analysis:
- AI Model: Large Convolutional Neural Network (CNN)
- Task: Distributed Training
- Key Operation: Intensive matrix multiplication
```
A. The HPE Data Fabric software component.
B. NVIDIA GPUs featuring multiple Tensor Cores.
C. An NVIDIA NVLink Bridge to connect the GPUs.
D. HPE ProLiant DL325 servers for the worker nodes.
E. HPE GreenLake for File Storage with standard NFS over TCP/IP.
Answer: B, C
16.An enterprise architecture team is debating the best method to adapt a general-purpose Large
Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly
changing knowledge base of product manuals and technical notes. Verifiability and traceability of the
information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding
style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)
A. Use both RAG and fine-tuning for both use cases as they are always used together.
B. Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
C. Use Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable
information at inference time.
D. Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.
E. Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the
model's behavior.
Answer: C, E
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17.A customer needs a solution for two primary workloads: large-scale model training and real-time
inference. They have a team of data scientists who are constantly developing new models and a separate
operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud
AI solution?
A. The entire process for all stakeholders is managed through the server's iLO interface.
B. Both the data scientists and the operations team would use the HPE Intelligent Configurator to manage
the models.
C. The data scientists would use NVIDIA NIMs to train the models, and the operations team would use
HPE Data Fabric to serve them.
D. The data scientists would primarily use tools within HPE AI Essentials (like JupyterLab and MLFlow) for
model development, while the operations team would use NVIDIA NIMs to deploy the finished models.
Answer: D
18.An architect is in a discovery call with a customer who describes their project: "Our primary goal is to
take our massive, proprietary dataset of chemical compound interactions and continuously update our
foundational AI model's internal parameters to create a new, specialized model for drug discovery. This
process runs 24/7 on a large GPU cluster."
How should the architect classify this primary AI workload?
A. Retrieval-Augmented Generation (RAG)
B. Edge Computing
C. AI Inferencing
D. AI Model Training / Fine-tuning
Answer: D
19.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. HPE ProLiant DL380a Gen11 servers with NVIDIA H100 NVL GPUs.
B. 8 worker nodes.
C. 4 worker nodes.
D. HPE ProLiant DL380a Gen11 servers with NVIDIA L40S GPUs.
E. HPE Cray compute nodes.
Answer: A, C
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