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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'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.
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 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.

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Authentic SAP C-AIG-2412 Exam Questions

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q27-Q32):

NEW QUESTION # 27
What are the benefits of SAP's generative Al hub?
Note: There are 2 correct answers to this question.

A. Send your data to various LLM providers for training feedback
B. Accelerate Al development with flexible access to a broad range of models
C. Provide libraries for no-code development
D. Build custom Al solutions and extend SAP applications

Answer: B,D

NEW QUESTION # 28
What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question.

A. Continuous delivery and tenant isolation for scalability
B. Integration of Al services with business applications using a standardized API
C. Monitoring and retraining models in SAP Al Core
D. Management of SAP S/4HANA cloud infrastructure
E. Orchestration of Al workflows such as model training and inference

Answer: A,B,E

NEW QUESTION # 29
What defines SAP's approach to LLMs?

A. Focusing solely on reducing the computational cost of training LLMs
B. Ensuring ethical AI practices and seamless business integration
C. Prioritizing the development of proprietary LLMs with no integration to existing systems
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 # 30
What can be done once the training of a machine learning model has been completed in SAP AICore? Note:
There are 2 correct answers to this question.

A. The model can be deployed in SAP HANA.
B. The model can be registered in the hyperscaler object store.
C. The model can be deployed for inferencing.
D. The model's accuracy can be optimized directly in SAP HANA.

Answer: B,C

Explanation:
Once the training of a machine learning model has been completed in SAP AI Core, several post-training actions can be undertaken to operationalize and manage the model effectively.
1. Deploying the Model for Inferencing:
* Deployment Process:After training, the model can be deployed as a service to handle inference requests. This involves setting up a model server that exposes an endpoint for applications to send data and receive predictions.
* Integration:The deployed model can be integrated into business applications, enabling real-time decision-making based on the model's predictions.

NEW QUESTION # 31
How does SAP deal with vulnerability risks created by generative Al? Note: There are 2 correct answers to this question.

A. By relying on external vendors to manage security threats.
B. By identifying human, technical, and exfiltration risks through an Al Security Taskforce.
C. By focusing on technological advancement only.
D. By implementing responsible Al use guidelines and strong product security standards.

Answer: B,D

NEW QUESTION # 32
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