HPE2-B08 Kostenlos Downloden, HPE2-B08 Deutsch Prüfungsfragen

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HP HPE2-B08 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Infrastructure Components of HPE Private Cloud AI with NVIDIA	20%	- Infrastructure capabilities for AI workload requirements - Benefits of HPE and NVIDIA integrated infrastructure
Topic 2: Customer Assessment and Solution Positioning	15%	- Assess AI maturity, workload characteristics and use cases - Position appropriate HPE AI solutions
Topic 3: Solution Sizing and Configuration	17%	- Differences between configuration sizes and options - Building configurations via One Config Advanced (OCA) - Using HPE Intelligent Configurator for sizing
Topic 4: Fundamental AI Concepts	28%	- Impact of AI on industries and infrastructure requirements - General AI concepts, applications and workloads
Topic 5: Software Components of HPE Private Cloud AI with NVIDIA	20%	- Benefits of HPE and NVIDIA software stack - Software functions supporting AI operations

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HPE2-B08 Deutsch Prüfungsfragen & HPE2-B08 Prüfung

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HPE Private Cloud AI Solutions HPE2-B08 Prüfungsfragen mit Lösungen (Q40-Q45):

40. Frage
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:
```
- 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?

A. The learning rate used for training was set too low.
B. The model has too few parameters to learn the features effectively.
C. The model is overfitting to the training data and cannot generalize to new data.
D. The model is underfitting due to an insufficient number of training epochs.

Antwort: C

41. Frage
An architect is using the HPE One Config Advanced (OCA) Smart Template for "HPE Private Cloud AI
- Small - Standard".
What is the worker node configuration they should expect to see in the generated Bill of Materials (BOM)?

A. 4x HPE ProLiant DL380a Gen11 servers with a total of 16x NVIDIA H100 NVL GPUs
B. 2x HPE ProLiant DL380a Gen11 servers with a total of 8x NVIDIA L40S GPUs
C. 1x HPE ProLiant DL380a Gen11 server with 4x NVIDIA L40S GPUs
D. 3x HPE ProLiant DL325 Gen11 servers for management

Antwort: C

42. Frage
The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG

A. 1-c, 2-a, 3-b
B. 1-b, 2-a, 3-c
C. 1-b, 2-c, 3-a
D. 1-a, 2-b, 3-c

Antwort: C

43. Frage
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 Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
B. Use both RAG and fine-tuning for both use cases as they are always used together.
C. Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.
D. Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
E. Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the model's behavior.

Antwort: A,E

44. Frage
A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
```
Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
```

A. NVIDIA RAPIDS
B. NVIDIA NeMo
C. NVIDIA NIM (NVIDIA Inference Microservices)
D. NVIDIA Triton Inference Server

Antwort: B

45. Frage
......

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