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In the PDF version, the SAP Certified Associate - SAP Generative AI Developer (C_AIG_2412) exam questions are printable and portable. You can take these SAP Certified Associate - SAP Generative AI Developer (C_AIG_2412) pdf dumps anywhere and even take a printout of SAP Certified Associate - SAP Generative AI Developer (C_AIG_2412) exam questions. The PDF version is mainly composed of real SAP C_AIG_2412 Exam Dumps. Lead2Passed updates regularly to improve its SAP Certified Associate - SAP Generative AI Developer (C_AIG_2412) pdf questions and also makes changes when required.

SAP C_AIG_2412 Exam Syllabus Topics:

Topic
Details

Topic 1

Advanced AI Techniques with SAP’s Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers advanced techniques available through SAP’s Generative AI Hub. Candidates are assessed on their ability to design, optimize, and scale generative AI solutions that go beyond basic implementations. The focus includes applying sophisticated strategies to integrate advanced models, manage performance, and align AI-driven outcomes with complex enterprise goals.

Topic 2

SAP AI Core: This section of the exam measures the skills of AI Developers and covers the fundamental components of SAP AI Core. Candidates are assessed on their ability to work with the core services that allow machine learning models to be deployed and managed within SAP environments. The focus is on understanding how AI Core fits into SAP’s ecosystem and ensures smooth integration with enterprise applications.

Topic 3

Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the practical use of large language models in SAP environments. Candidates are expected to understand how LLMs can be applied to automate tasks, enhance decision-making, and improve user interaction within SAP systems. The exam evaluates knowledge of handling model selection, fine-tuning, and adapting LLMs to specific business cases.

Topic 4

SAP's Generative AI Hub: This section of the exam measures the skills of Solution Architects and covers SAP’s Generative AI Hub, which acts as the central layer for designing and managing generative AI solutions. The exam tests knowledge of building, deploying, and connecting AI models to business scenarios through the Hub. Emphasis is placed on leveraging the Hub to streamline workflows and ensure scalable solutions that align with organizational needs.

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Flexible C_AIG_2412 Testing Engine & C_AIG_2412 Sample Questions

This pdf covers all of the C_AIG_2412 Exam Questions from the previous exams as well as those that will appear in the upcoming SAP C_AIG_2412 exam. The C_AIG_2412 PDF exam questions are compiled according to the latest exam syllabus to ensure your success. The SAP C_AIG_2412 PDF exam questions are also printable to make handy notes.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q53-Q58):

NEW QUESTION # 53
What capabilities does the Exploration and Development feature of the generative Al hub provide? Note:
There are 2 correct answers to this question.

A. Automatic model selection
B. Develop and debug ABAP code
C. Prompt editor and management
D. Al playground and chat

Answer: C,D

Explanation:
The Exploration and Development feature of SAP's Generative AI Hub provides several capabilities to facilitate AI solution development:
1. AI Playground and Chat:
* Interactive Environment:The AI playground offers an interactive space for developers to experiment with various AI models, test prompts, and observe outputs in real-time.
* Conversational Interface:The chat functionality enables users to engage in dialogue with AI models, refining prompts and understanding model behavior through iterative interactions.
2. Prompt Editor and Management:
* Prompt Creation:The prompt editor allows developers to craft and modify prompts tailored to specific business needs, enhancing the precision of AI responses.
* Prompt Organization:Prompt management tools facilitate the organization, versioning, and storage of prompts, ensuring efficient retrieval and reuse in various projects.

NEW QUESTION # 54
Which of the following techniques uses a prompt to generate or complete subsequent prompts (streamlining the prompt development process), and to effectively guide Al model responses?

A. One-shot prompting
B. Few-shot prompting
C. Meta prompting
D. Chain-of-thought prompting

Answer: C

Explanation:
Meta prompting is a technique in prompt engineering where a prompt is designed to generate or refine subsequent prompts.
1. Definition and Purpose:
* Streamlining Prompt Development:Meta prompting automates the creation of effective prompts by utilizing AI to generate or enhance them, thereby streamlining the prompt development process.
* Guiding AI Model Responses:By generating refined prompts, meta prompting effectively guides AI models to produce more accurate and contextually relevant responses.
2. Application in SAP's Generative AI Hub:
* Prompt Engineering Tools:SAP's Generative AI Hub provides tools that support advanced prompt engineering techniques, including meta prompting, to enhance AI model interactions.

NEW QUESTION # 55
What are some benefits of SAP Business Al? Note: There are 3 correct answers to this question.

A. Automatic human emotion recognition
B. Intelligent business document processing
C. Face detection and face recognition
D. Personalized recommendations based on Al algorithms
E. Al-powered forecasting and predictions

Answer: B,D,E

Explanation:
SAP Business AI offers a suite of capabilities designed to enhance various business processes through intelligent automation and data-driven insights.
1. Intelligent Business Document Processing:
* Document Information Extraction:SAP Business AI includes services that automate the extraction of relevant information from business documents, such as invoices and purchase orders. This automation reduces manual data entry, minimizes errors, and accelerates processing times.
2. AI-Powered Forecasting and Predictions:
* Predictive Analytics:SAP Business AI leverages machine learning models to analyze historical data and predict future trends. This capability assists businesses in demand forecasting, financial planning, and inventory management, enabling proactive decision-making.
3. Personalized Recommendations Based on AI Algorithms:
* Personalized Recommendation Services:By analyzing user behavior and preferences, SAP Business AI provides personalized product or service recommendations. This personalization enhances customer experience and can lead to increased sales and customer satisfaction.

NEW QUESTION # 56
What is the purpose of splitting documents into smaller overlapping chunks in a RAG system?

A. To enable the matching of different relevant passages to user queries
B. To reduce the storage space required for the vector database
C. To improve the efficiency of encoding queries into vector representations
D. To simplify the process of training the embedding model

Answer: A

Explanation:
In Retrieval-Augmented Generation (RAG) systems, splitting documents into smaller overlapping chunks is a crucial preprocessing step that enhances the system's ability to match relevant passages to user queries.
1. Purpose of Splitting Documents into Smaller Overlapping Chunks:
* Improved Retrieval Accuracy:Dividing documents into smaller, manageable segments allows the system to retrieve the most relevant chunks in response to a user query, thereby improving the precision of the information provided.
* Context Preservation:Overlapping chunks ensure that contextual information is maintained across segments, which is essential for understanding the meaning and relevance of each chunk in relation to the query.
2. Benefits of This Approach:
* Enhanced Matching:By having multiple overlapping chunks, the system increases the likelihood that at least one chunk will closely match the user's query, leading to more accurate and relevant responses.
* Efficient Processing:Smaller chunks are easier to process and analyze, enabling the system to handle large documents more effectively and respond to queries promptly.

NEW QUESTION # 57
Which of the following is a benefit of using Retrieval Augmented Generation?

A. It enables LLMs to learn new languages without additional training.
B. It reduces the computational resources required for language modeling.
C. It eliminates the need for fine-tuning LLMs for specific tasks.
D. It allows LLMs to access and utilize information beyond their initial training data.

Answer: D

Explanation:
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data.
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. Benefits of RAG:
* Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses.
* 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.
* Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.

NEW QUESTION # 58
......

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