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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q32-Q37):

NEW QUESTION # 32
What are some use cases for fine-tuning of a model?
Note: There are 2 correct answers to this question.

A. To quickly create iterations on a new use case
B. To introduce new knowledge to a model in a resource-efficient way
C. To sanitize model outputs
D. To customize outputs for specific types of inputs

Answer: B,D

NEW QUESTION # 33
Which neural network architecture is primarily used by LLMs?

A. Transformer architecture with self-attention mechanisms
B. Sequential encoder-decoder architecture
C. Recurrent neural network architecture
D. Convolutional Neural Networks (CNNs)

Answer: A

NEW QUESTION # 34
What are some benefits of the SAP AI Launchpad? Note: There are 2 correct answers to this question.

A. Direct deployment of Al models to SAP HANA.
B. Simplified model retraining and performance improvement.
C. Integration with non-SAP platforms like Azure and AWS.
D. Centralized Al lifecycle management for all Al scenarios.

Answer: B,D

NEW QUESTION # 35
What is the goal of prompt engineering?

A. To develop new neural network architectures for Al models
B. To optimize hardware performance for Al computations
C. To replace human decision-making with automated processes
D. To craft inputs that guide Al systems in generating desired outputs

Answer: D

Explanation:
Prompt engineering involves designing and refining inputs, known as prompts, to effectively guide AI systems, particularly Large Language Models (LLMs), in producing desired outputs.
1. Understanding Prompt Engineering:
* Definition:Prompt engineering is the process of creating and optimizing prompts to elicit specific responses from AI models. It serves as a crucial interface between human intentions and machine- generated content.
* Purpose:The primary goal is to communicate the task requirements clearly to the AI model, ensuring that the generated output aligns with user expectations.
2. Importance in AI Systems:
* Guiding AI Behavior:Well-crafted prompts can direct AI models to perform a wide range of tasks, from answering questions to generating creative content, by setting the context and specifying the desired format of the output.
* Enhancing Output Quality:Effective prompt engineering can improve the relevance, coherence, and accuracy of AI-generated responses, making AI systems more useful and reliable in practical applications.
3. Application in SAP's Generative AI Hub:
* Prompt Management:SAP's Generative AI Hub provides tools for prompt management, allowing developers to create, edit, and manage prompts to interact with various AI models efficiently.
* Exploration and Development:The hub offers features like prompt editors and AI playgrounds, enabling users to experiment with different prompts and models to achieve optimal results for their specific use cases.

NEW QUESTION # 36
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

A. The user's questi on is used to search a knowledge base or a set of documents.
B. The retrieved content is combined with the LLM's capabilities to generate a response.
C. The LLM directly ans wers the user's question without accessing external information.
D. The embedding model stores the generated ans wers for future reference.

Answer: A,B

Explanation:
Retrieval-Augmented Generation (RAG) is a process that enhances the capabilities of Large Language Models (LLMs) by integrating external knowledge sources into the response generation process.
1. Understanding the RAG Process:
* User Query:The process begins with a user's question or prompt, which serves as the input for the system.
* Retrieval Step:The system uses the user's query to search a knowledge base or a set of documents, retrieving relevant information that can inform the response.
* Integration with LLM:The retrieved content is then combined with the LLM's inherent knowledge and language generation capabilities to produce a comprehensive and contextually relevant response.
2. Benefits of the RAG Process:
* Enhanced Accuracy:By incorporating up-to-date and domain-specific information from external sources, RAG improves the accuracy of AI-generated responses.
* Contextual Relevance:The integration of retrieved data ensures that the responses are more aligned with the specific context of the user's query.
3. Application in SAP's Generative AI Hub:
* Generative AI Hub SDK:SAP provides a Generative AI Hub SDK that facilitates the implementation of RAG by enabling seamless integration of retrieval mechanisms with LLMs.
* Tutorials and Resources:SAP offers tutorials, such as "Retrieval Augmented Generation using generative-ai-hub-sdk and HANA vector search," to guide developers in implementing RAG systems effectively.

NEW QUESTION # 37
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Valid C_AIG_2412 Exam Questions That Have Been Tried and True

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q32-Q37):

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