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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	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 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	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.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q22-Q27):

NEW QUESTION # 22
What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question.

A. Pipeline containers to be used
B. Deployment templates for SAP AI Launchpad
C. User scripts to manually execute pipeline steps
D. Infrastructure resources such as CPUs or GPUs

Answer: A,D

Explanation:
When training a machine learning model using SAP AI Core, defining an executable requires specifying certain key components to ensure the training process is efficient and effective.
1. Pipeline Containers to Be Used:
* Definition:Pipeline containers encapsulate the environments and dependencies necessary for each step of the machine learning workflow, including data preprocessing, model training, and evaluation.
* Specification in Executable:Within the executable, it's essential to define which pipeline containers will be utilized to ensure that each stage of the training process has the appropriate tools and libraries.

NEW QUESTION # 23
How does the Al API support SAP AI scenarios? Note: There are 2 correct answers to this question.

A. By managing Kubernetes clusters automatically
B. By integrating Al services into business applications
C. By providing a unified framework for operating Al services
D. By integrating Al models into third-party platforms like AWS

Answer: B,C

Explanation:
The AI API in SAP plays a crucial role in supporting AI scenarios by facilitating the integration of AI services into business applications and providing a unified framework for operating these services.
1. Integration of AI Services into Business Applications:
* Seamless Integration:The AI API enables developers to incorporate AI functionalities directly into SAP and non-SAP business applications, enhancing their capabilities with intelligent features.
* Use Case Example:Integrating AI models into SAP Build Apps allows for the creation of AI-powered applications that can perform tasks such as data analysis, predictive modeling, and more.
2. Unified Framework for Operating AI Services:
* Standardized Operations:The AI API provides a consistent and standardized framework for managing AI services, ensuring uniformity in deployment, monitoring, and maintenance across different platforms.
* Scalability and Flexibility:This unified approach allows for scalable AI operations, accommodating various AI models and services within a cohesive operational structure.

NEW QUESTION # 24
What is the goal of prompt engineering?

A. To optimize hardware performance for Al computations
B. To craft inputs that guide Al systems in generating desired outputs
C. To replace human decision-making with automated processes
D. To develop new neural network architectures for Al models

Answer: B

Explanation:
Prompt engineering involves designing and refining inputs, known as prompts, to effectively guide AI systems, particularly Large Language Models (LLMs), in producing desired outputs.
1. Understanding Prompt Engineering:
* Definition:Prompt engineering is the process of creating and optimizing prompts to elicit specific responses from AI models. It serves as a crucial interface between human intentions and machine- generated content.
* Purpose:The primary goal is to communicate the task requirements clearly to the AI model, ensuring that the generated output aligns with user expectations.
2. Importance in AI Systems:
* Guiding AI Behavior:Well-crafted prompts can direct AI models to perform a wide range of tasks, from answering questions to generating creative content, by setting the context and specifying the desired format of the output.
* Enhancing Output Quality:Effective prompt engineering can improve the relevance, coherence, and accuracy of AI-generated responses, making AI systems more useful and reliable in practical applications.
3. Application in SAP's Generative AI Hub:
* Prompt Management:SAP's Generative AI Hub provides tools for prompt management, allowing developers to create, edit, and manage prompts to interact with various AI models efficiently.
* Exploration and Development:The hub offers features like prompt editors and AI playgrounds, enabling users to experiment with different prompts and models to achieve optimal results for their specific use cases.

NEW QUESTION # 25
Which of the following are functionalities provided by the generative-Al-hub-SDK ?
Note: There are 2 correct answers to this question.

A. Configure SAP BTP credentials
B. Customize SAP AI Launchpad
C. Create chat responses and embeddings
D. Interact with LLMs

Answer: C,D

NEW QUESTION # 26
What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question.

A. Pipeline containers to be used
B. Deployment templates for SAP AI Launchpad
C. User scripts to manually execute pipeline steps
D. Infrastructure resources such as CPUs or GPUs

Answer: A,D

NEW QUESTION # 27
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