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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q23-Q28):

NEW QUESTION # 23
What defines SAP's approach to LLMs?

A. Ensuring ethical AI practices and seamless business integration
B. Prioritizing the development of proprietary LLMs with no integration to existing systems
C. Focusing solely on reducing the computational cost of training LLMs
D. Limiting LLM usage to non-business applications only

Answer: A

Explanation:
SAP's approach to Large Language Models (LLMs) is centered on integrating these powerful AI tools into its enterprise ecosystem while adhering to ethical standards. Unlike option A, SAP does not focus solely on proprietary LLMs without integration; instead, it leverages both proprietary and third-party models (e.g., via partnerships with providers like Azure OpenAI) to enhance business applications. Option B is incorrect because reducing computational cost is not the sole focus-SAP prioritizes value delivery through integration with business processes. Option D is also inaccurate, as SAP explicitly targets business applications rather than limiting LLMs to non-business use. Option C is correct because SAP emphasizes ethical AI practices (e.
g., through its AI Ethics Policy) and seamless integration with tools like SAP S/4HANA and SAP SuccessFactors, ensuring LLMs enhance enterprise workflows responsibly and effectively.

NEW QUESTION # 24
Which of the following is a principle of effective prompt engineering?

A. Use precise language and providing detailed context in prompts.
B. Combine multiple complex tasks into a single prompt.
C. Keep prompts as short as possible to avoid confusion.
D. Write vague and open-ended instructions to encourage creativity.

Answer: A

NEW QUESTION # 25
Which of the following capabilities does the generative Al hub provide to developers? Note: There are 2 correct answers to this question.

A. Tools for prompt engineering and experimentation
B. Proprietary LLMs exclusively
C. Integration of foundation models into applications
D. Code generation to extend SAP BTP applications

Answer: A,C

Explanation:
* C. Tools for prompt engineering and experimentation:Generative AI hubs often provide tools and resources to help developers refine their prompts. This is crucial because the quality of the output from a generative AI model heavily depends on how well the prompt is crafted. These tools might include:
* Prompt libraries:Collections of effective prompts for various tasks.
* Prompt testing and analysis:Features to test different prompts and analyze the AI's response.
* Guides and tutorials:Resources to learn about prompt engineering best practices.
* D. Integration of foundation models into applications:Generative AI hubs make it easier for developers to integrate powerful foundation models (large language models like those from Google, OpenAI, etc.) into their own applications. This means developers don't have to build these complex models from scratch. Instead, they can leverage existing models and customize them for their specific needs. This might involve:
* APIs and SDKs:Providing easy-to-use interfaces to access and interact with the foundation models.
* Model customization:Tools to fine-tune existing models on specific datasets or for particular tasks.
* Deployment options:Support for deploying AI models in different environments (cloud, on- premises, etc.).
Why the other options are incorrect:
* A. Proprietary LLMs exclusively:While some generative AI hubs might offer their own proprietary models, they usually provide access to a variety of models, including open-source and those from other providers. This gives developers more flexibility and choice.
* B. Code generation to extend SAP BTP applications:While code generation is a common feature of generative AI, it's not the primary focus of a generative AI hub. The hub's main purpose is to provide access to and facilitate the use of foundation models, not to specifically extend SAP BTP applications.

NEW QUESTION # 26
Which technique is used to supply domain-specific knowledge to an LLM?

A. Retrieval-Augmented Generation
B. Fine-tuning the model on general data
C. Prompt template expansion
D. Domain-adaptation training

Answer: A

Explanation:
Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by integrating external domain-specific knowledge, enabling more accurate and contextually relevant outputs.
1. Understanding Retrieval-Augmented Generation (RAG):
* Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses.
* Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response.
2. Application in Supplying Domain-Specific Knowledge:
* Domain Adaptation:By leveraging RAG, LLMs can access specialized information without the need for extensive retraining or fine-tuning. This approach is particularly beneficial for domains with rapidly evolving information or where incorporating proprietary data is essential.
* Efficiency:RAG enables models to provide informed responses by referencing external data, reducing the necessity for large-scale domain-specific training datasets and thereby conserving computational resources.
3. Advantages of Using RAG:
* Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields.
* Enhanced Accuracy:Incorporating external knowledge allows the model to produce more precise and contextually relevant outputs, especially in specialized domains.

NEW QUESTION # 27
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

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

Answer: A,B

NEW QUESTION # 28
......

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Test C-AIG-2412 Questions Answers - C-AIG-2412 Valid Exam Cram

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q23-Q28):

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