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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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 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 4	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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q60-Q65):

NEW QUESTION # 60
What are some drivers for the rapid adoption of generative AI?
Note: There are 2 correct answers to this question.

A. Significant hardware cost savings
B. Availability of skilled developers
C. Wide availability
D. Ease of use

Answer: C,D

NEW QUESTION # 61
Which technique is used to supply domain-specific knowledge to an LLM?

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

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 # 62
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.

A. Store the dataset in the SAP HANA Vector Engine.
B. Store the dataset in a hyperscaler object store.
C. Grant access rights to the SAP BTP cockpit.
D. Provide the storage secret to access the dataset.

Answer: B,D

NEW QUESTION # 63
What defines SAP's approach to LLMs?

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

Answer: B

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 # 64
What is Machine Learning (ML)?

A. A form of Al that only focuses on creating new content, including text, images, sound, and videos.
B. A statistical method for data processing that does not involve any Al techniques.
C. A technology that equips machines with human-like capabilities such as problem-solving, visual perception, and decision-making.
D. A subset of Al that focuses on enabling computer systems to learn and improve from experience or data.

Answer: D

Explanation:
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that empowers computer systems to learn from data and experiences, enhancing their performance over time without explicit programming for each task.
1. Definition and Core Concept:
* Learning from Data:ML algorithms process and analyze large datasets to identify patterns and make informed decisions or predictions based on new, unseen data.
* Improvement Over Time:Through iterative processes, ML models refine their accuracy and efficiency as they are exposed to more data, leading to continuous performance enhancement.
2. Types of Machine Learning:
* Supervised Learning:Models are trained on labeled datasets, where the desired output is known, to make predictions or classifications.
* Unsupervised Learning:Models work with unlabeled data to identify inherent structures or patterns without predefined outcomes.
* Reinforcement Learning:Systems learn by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting actions accordingly.
3. Applications in SAP's AI Solutions:
* SAP AI Core and AI Launchpad:SAP provides a unified framework for managing and deploying ML models, facilitating seamless integration into business processes.
* Generative AI Hub:This platform offers access to a variety of large language models (LLMs) and supports the orchestration of AI tasks, enabling the development of AI-driven applications.

NEW QUESTION # 65
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