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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	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 3	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 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.

SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q52-Q57):

NEW QUESTION # 52
What are some components of the training pipeline in SAP AI Core?
Note: There are 2 correct answers to this question.

A. Executables that define the training process
B. Automated deployment to Kubernetes clusters
C. Input datasets stored in a hyperscaler object store
D. The SAP HANA database for model storage

Answer: A,C

NEW QUESTION # 53
Which technique is used to supply domain-specific knowledge to an LLM?

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

Answer: C

NEW QUESTION # 54
What is a part of LLM context optimization?

A. Enhancing the computational speed of the model
B. Adjusting the model's output format and style
C. Reducing the model's size to improve efficiency
D. Providing the model with domain-specific knowledge needed to solve a problem

Answer: D

NEW QUESTION # 55
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. Monitoring and retraining models in SAP Al Core
C. Management of SAP S/4HANA cloud infrastructure
D. Integration of Al services with business applications using a standardized API
E. Orchestration of Al workflows such as model training and inference

Answer: A,D,E

NEW QUESTION # 56
What is a part of LLM context optimization?

A. Enhancing the computational speed of the model
B. Adjusting the model's output format and style
C. Reducing the model's size to improve efficiency
D. Providing the model with domain-specific knowledge needed to solve a problem

Answer: D

Explanation:
LLM context optimization involves tailoring a Large Language Model's (LLM) input context to enhance its performance on specific tasks, particularly by incorporating domain-specific knowledge.
1. Understanding LLM Context Optimization:
* Definition:Context optimization refers to the process of adjusting the input provided to an LLM to ensure it includes relevant information, thereby enabling the model to generate more accurate and contextually appropriate outputs.
* Domain-Specific Knowledge Integration:By embedding domain-specific information into the model's context, the LLM can better understand and address specialized queries, leading to improved problem- solving capabilities.
2. Importance of Domain-Specific Knowledge:
* Enhanced Relevance:Providing domain-specific context ensures that the model'sresponses are pertinent to the particular field or subject matter, increasing the utility of the generated content.
* Improved Accuracy:With access to specialized knowledge, the LLLM is less likely to produce generic or incorrect answers, thereby enhancing the overall quality of its outputs.
3. Methods of Context Optimization:
* Prompt Engineering:Crafting prompts that include necessary domain-specific information to guide the model towards generating desired responses.
* Retrieval-Augmented Generation (RAG):Incorporating external data sources into the model's context to provide up-to-date and relevant information pertinent to the domain.

NEW QUESTION # 57
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