Get Certified Easily with Online HPE2-B08 Dumps

Using HPE Private Cloud AI Solutions HPE2-B08 questions can help you build confidence as you prepare for the exam. By practicing with these HPE2-B08 dumps questions, you will be better prepared and more confident when you take the actual exam. Besides, HPE2-B08 exam dumps questions cover a wide range of topics that are relevant to the certification exam. By using HPE2-B08 dumps questions, you will be able to ensure that you have a comprehensive understanding of the material covered on the exam. Practice free HP HPE2-B08 exam dumps below.

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1. How do HPE Intelligent Configurator and OCA help in AI system selection? (Select two.)

By optimizing AI hardware based on workload needs

By automatically assigning GPUs and CPUs

By eliminating the need for manual AI model tuning

By simplifying AI infrastructure sizing and purchasing decisions

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2. What are the three primary types of AI?

Narrow AI, General AI, and Super AI

Rule-Based AI, Neural AI, and Statistical AI

Cloud AI, Edge AI, and Hybrid AI

Predictive AI, Descriptive AI, and Prescriptive AI

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3. What is the primary factor when selecting the correct configuration size for an HPE Private Cloud AI solution?

AI workload requirements and scalability needs

The number of IT administrators managing the system

Network bandwidth limitations

Storage array type only

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4. A company wants to build an AI-powered chatbot for customer support.

Which AI approach should they use?

Rule-based programming

Natural Language Processing (NLP)

Manual customer service responses

Static web scripts

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5. How does HPE Ezmeral contribute to AI workflow management? (Select two.)

Container-based AI model deployment

Requiring manual provisioning for every AI model

Restricting AI workloads to a single cloud provider

Integration with Kubernetes for AI workloads

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6. Which workload is best suited for AI processing?

Static web hosting

Video and image recognition

Network storage backup

Simple database queries

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7. A retail company wants to deploy an AI-powered recommendation engine that can scale dynamically with customer data growth.

What is the best approach?

Private cloud AI with scalable storage and compute resources

On-premises fixed-size AI deployment

Manual dataset review without AI integration

Restricting AI inferencing to predefined datasets

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8. In which industries is AI commonly used? (Select two.)

Healthcare for predictive diagnostics

Retail for supply chain management

Manual data entry automation

Traditional file storage management

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9. A hospital wants to use AI for diagnosing diseases from medical images.

What key considerations should they account for? (Select two.)

High-quality labeled training datasets

Deep learning models for image recognition

Manual review without AI assistance

Avoiding automation for better accuracy

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10. What are key benefits of HPE AI-optimized infrastructure? (Select two.)

Scalable AI processing with NVIDIA GPUs

Integrated AI inferencing with low-latency networking

Limiting AI workloads to fixed configurations

Eliminating the need for AI model training

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11. What is a key infrastructure requirement for AI workloads?

High-performance GPUs for accelerated computation

Manual configuration of storage devices

Static CPU-based architecture

Limited scalability for AI deployments

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12. What are key components required to build an AI system? (Select two.)

Manual decision trees

Static rules instead of dynamic models

Large datasets for model training

GPUs for accelerated computation

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13. A retail company wants to implement AI-driven demand forecasting and needs an integrated AI software stack.

Which solution should they use?

Manual spreadsheet-based forecasting

On-premises HDD storage without AI optimization

Traditional CPU-based web hosting infrastructure

HPE Ezmeral AI for containerized AI workloads

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14. What are key networking considerations for AI workloads in HPE Private Cloud AI? (Select two.)

Low-latency networking for high-speed AI data transfers

Limiting AI workloads to basic Ethernet connections

High-bandwidth interconnects like InfiniBand for large AI models

Using standard LAN infrastructure without optimization

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15. What are key differences between AI training and inference? (Select two.)

Training requires large datasets, while inference uses pre-trained models

Inference always produces the same output regardless of input data

Training is typically done on CPUs, while inference is GPU-based

Inference is real-time and optimized for low-latency predictions

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16. What is a key advantage of using a large-scale HPE Private Cloud AI configuration?

Lower latency in manual data processing

Greater parallel processing capability for complex AI models

Reduced need for automation in AI workloads

Eliminates AI model retraining needs

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17. A global enterprise wants a scalable, cloud-based AI platform that integrates with hybrid environments.

Which HPE solution should they deploy?

HPE GreenLake AI

On-premises HDD storage

Legacy CPU-based infrastructure

Single-server AI execution

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18. A financial institution wants to use AI for fraud detection.

What AI techniques should they consider? (Select two.)

Static rule-based systems

Neural networks for pattern recognition

Anomaly detection algorithms

Manual transaction reviews

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19. What are key storage considerations when selecting an AI configuration? (Select two.)

Restricting storage capacity to fixed sizes

Scalable storage for growing AI datasets

High-speed NVMe storage for real-time AI data processing

Using HDDs exclusively for AI workloads

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20. Which deep learning frameworks are commonly used for AI development? (Select two.)

TensorFlow

Apache Hadoop

PyTorch

MySQL

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21. What is a key benefit of the HPE and NVIDIA partnership for AI workloads?

Manual configuration of GPUs for AI training

Elimination of the need for high-performance computing (HPC) clusters

Reduced dependency on cloud-based AI processing

Optimized hardware and software integration for AI acceleration

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22. How does NVIDIA AI Enterprise enhance AI workloads on HPE Private Cloud AI?

By providing enterprise-grade software optimizations for AI and deep learning

By replacing the need for AI-specific hardware

By restricting AI workloads to on-premises environments

By automating all AI model decisions without human intervention

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23. A data science team is deploying an AI-based predictive analytics model that requires extensive deep learning computations.

Which HPE solution should they choose?

HDD-based storage arrays with no GPU integration

Traditional x86 servers with limited compute acceleration

HPE Apollo 6500 with NVIDIA GPUs

Wireless networking for high-speed model execution

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24. How does One Config Advanced (OCA) help in building an HPE Private Cloud AI configuration?

By automating AI training pipelines

By replacing manual AI workload tuning

By selecting optimal hardware and software components

By enforcing static AI configurations

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25. What is a critical storage requirement for AI workloads in HPE Private Cloud AI?

High-bandwidth, low-latency storage such as HPE Alletra or HPE Cray ClusterStor

Traditional HDD storage for long-term archiving

Optical storage for quick read access

Isolated storage nodes without data movement capabilities

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