Latest [Oct 31, 2026] Realistic Verified HPE2-B08 Dumps [Q38-Q55]

QUESTION 38
A customer states they need an AI solution for a “summarization” use case that will be accessed by approximately 75 concurrent users. The underlying data is updated frequently, so RAG will be required.
When using the HPE Intelligent Configurator, which three inputs are mandatory to get an initial sizing recommendation?

QUESTION 39
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?

QUESTION 40
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?

QUESTION 41
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?

QUESTION 42
A customer is building a large-scale AI training cluster using HPE Private Cloud AI.
They have two main requirements for the storage solution:
1. It must provide extremely high throughput and low latency to feed data to a large cluster of NVIDIA H100 GPUs.
2. It must be able to scale performance and capacity independently to accommodate future growth without costly overprovisioning.
The architect is proposing HPE GreenLake for File Storage.
Which architectural features of this solution directly address the customer’s requirements? (Select all that apply.)
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Customer Requirements:
1. High performance for AI training workloads.
2. Independent scaling of performance and capacity.
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QUESTION 43
What is the primary architectural advantage of the NVIDIA Grace Hopper Superchip (e.g., GH200) for large-scale AI workloads?

QUESTION 44
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?

QUESTION 45
An organization is using HPE Private Cloud AI to manage a large, multi-tenant Kubernetes environment for its data science teams. They need to ensure that data access is securely managed and that each team’s data is isolated within a logical, policy-driven boundary. Additionally, they need to store and access data from multiple sources, including NFS clients and S3-compatible object stores.
Which software components of the HPE Private Cloud AI stack work together to provide this unified, policy-driven, multi-protocol data access? (Select all that apply.)

QUESTION 46
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?

QUESTION 47
A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)

QUESTION 48
A company is implementing a RAG-based chatbot using HPE Private Cloud AI. To ensure the chatbot provides safe and appropriate responses, the development team needs to implement guardrails to prevent it from discussing off-topic subjects and to block it from using harmful language.
Which specific toolkit within the NVIDIA NeMo framework is designed for this purpose?
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NVIDIA NeMo Framework Components:
1. NeMo Curator
2. NeMo Customizer
3. NeMo Evaluator
4. NeMo Retriever
5. NeMo Guardrails
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QUESTION 49
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:
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– 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?

QUESTION 50
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?

QUESTION 51
An enterprise wants to deploy pre-trained foundation models from various sources for multiple business units. A key requirement is to simplify and standardize the deployment process, regardless of the model’s origin. They want a solution that packages models into scalable, optimized, and easy-to-use microservices with a standard API.
Which component of the NVIDIA AI Enterprise software suite directly addresses this need?

QUESTION 52
A data science team has trained a deep learning model for image classification. While the model achieves 99.8% accuracy on the training dataset, its accuracy drops to only 75% on a new, unseen validation dataset.
The team provides the following training metrics:
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– Training Epochs: 500
– Training Dataset Size: 1,000 images
– Model Parameters: 15 million
– Training Accuracy: 99.8%
– Validation Accuracy: 75.3%
“`
What is the most likely cause of this performance discrepancy?

QUESTION 53
A customer needs to run a generative AI workload at multiple, dispersed edge locations. Each location has significant space and power constraints. The workload is inference-only and does not require the absolute highest performance, but rather a balance of good performance and energy efficiency.
Which HPE ProLiant server and NVIDIA GPU combination is specifically positioned for this type of edge AI use case?

QUESTION 54
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?

QUESTION 55
A financial services firm is building a fraud detection application on HPE Private Cloud AI. The application needs to process a continuous stream of transaction data from multiple sources in real time.
The data science team requires a robust platform to build, manage, and execute the complex data pipelines needed to feed the AI model.
Which pre-integrated open-source tool within HPE AI Essentials is the industry standard for orchestrating and managing these complex data workflows and pipelines?