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SAP C_AIG_2412 Exam Syllabus Topics:

Topic
Details

Topic 1

SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.

Topic 2

Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.

Topic 3

SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.

Topic 4

SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q41-Q46):

NEW QUESTION # 41
What are some characteristics of the SAP generative Al hub? Note: There are 2 correct answers to this question.

A. It ensures relevant, reliable, and responsible business Al.
B. It operates independently of SAP's partners and ecosystem.
C. It provides instant access to a wide range of large language models (LLMs).
D. It only supports traditional machine learning models.

Answer: A,C

NEW QUESTION # 42
Which technique is used to supply domain-specific knowledge to an LLM?

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

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 # 43
What are some metrics to evaluate the effectiveness of a Retrieval Augmented Generation system? Note:
There are 2 correct answers to this question.

A. Faithfulness
B. Speed
C. Carbon footprint
D. Relevance

Answer: A,D

Explanation:
Evaluating the effectiveness of a Retrieval-Augmented Generation (RAG) system involves assessing specific metrics that determine the quality and reliability of the generated content.
1. Faithfulness:
* Definition:Faithfulness measures the degree to which the generated output accurately reflects the information retrieved from source documents without introducing unsupported content.
* Importance:High faithfulness ensures that the system's responses are trustworthy and based on factual data, which is crucial for applications requiring precise information dissemination.
2. Relevance:
* Definition:Relevance assesses how pertinent the generated content is to the user's query or the task at hand.
* Importance:Ensuring relevance guarantees that the system provides information that directly addresses user needs, enhancing user satisfaction and system utility.
3. Application in RAG Systems:
* Performance Evaluation:By measuring faithfulness and relevance, developers can fine-tune RAG systems to produce outputs that are both accurate and pertinent, thereby improving overall system performance.
* User Trust:Maintaining high levels of these metrics fosters user trust, as the system consistently delivers reliable and contextually appropriate information.

NEW QUESTION # 44
Which statement best describes the Chain-of-Thought (COT) prompting technique?

A. Writing a series of connected prompts creating a chain of related information.
B. Connecting related concepts by having the LLM generate chains of ideas.
C. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.
D. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.

Answer: C

Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.

NEW QUESTION # 45
Which of the following statements accurately describe the RAG process? Note: There are 2 correct ans-wers to this question.

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

Answer: B,C

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 # 46
......

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