HPE2-B08최신시험최신덤프자료시험기출문제모음집

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HP HPE2-B08 Exam Syllabus Topics:

Section	Weight	Objectives
Infrastructure Components of HPE Private Cloud AI with NVIDIA	20%	- Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure
Fundamental AI Concepts	28%	- General AI concepts, applications and workloads - Impact of AI on industries and infrastructure requirements
Software Components of HPE Private Cloud AI with NVIDIA	20%	- Software functions supporting AI operations - Benefits of HPE and NVIDIA software stack
Customer Assessment and Solution Positioning	15%	- Position appropriate HPE AI solutions - Assess AI maturity, workload characteristics and use cases
Solution Sizing and Configuration	17%	- Using HPE Intelligent Configurator for sizing - Differences between configuration sizes and options - Building configurations via One Config Advanced (OCA)

>> HPE2-B08최신 시험 최신 덤프자료 <<

최신 Hybrid Cloud HPE2-B08 무료샘플문제 (Q27-Q32):

질문 # 27
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. AI Recommender System
C. Document Chat
D. Code Generation

정답：A

질문 # 28
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?

A. To provide a credit for future software purchases on the HPE GreenLake cloud.
B. To cover the pre-integration and configuration of the entire solution at an HPE factory before shipment.
C. To provide on-site training for the customer's data science team.
D. To allow the customer to exchange the hardware for the next generation at no cost.

정답：B

질문 # 29
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 can be trained without GPUs, unlike CNNs.
B. CNNs can only process images, not text.
C. CNNs require significantly more training data than Transformers.
D. Transformers use an attention mechanism to understand the contextual relationships between all words in the text, which is critical for summarization.

정답：D

질문 # 30
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. Send the output from the HPE Intelligent Configurator directly to the distribution partner for a quote.
C. Use the standard HPE ProLiant DL380a Gen11 server template in OCA and add the GPUs and networking manually.
D. Manually add each component (servers, GPUs, switches, cables, etc.) one by one into HPE One Config Advanced (OCA).

정답：A

질문 # 31
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 Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
C. Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
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.

정답：B,E

질문 # 32
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HPE2-B08최신시험최신덤프자료시험기출문제모음집

HP HPE2-B08 Exam Syllabus Topics:

최신버전 HPE2-B08최신 시험 최신 덤프자료 완벽한 시험 최신버전 덤프

최신 Hybrid Cloud HPE2-B08 무료샘플문제 (Q27-Q32):